That regulatory workloads are soaring across the international life sciences industry is well acknowledged. A new survey, conducted for ArisGlobal by Censuswide with senior regulatory professionals in US pharma and biopharma organizations, confirms not only that almost all have seen their regulatory obligations expand over the last five years, three in five (60%) put the increase beyond what would be expected as the result of company growth. A trend that is not showing any signs of waning.

It is why the industry is turning purposefully now toward artificial intelligence (AI), and in particular next-generation technologies such as Generative AI (GenAI) powered by large language models (LLMs), albeit with some lingering reservations about their current potential.

AI’s role in the regulatory function

The good news is that there is now widespread acceptance of AI’s potential usefulness in solving information or process bottlenecks in a regulatory context, with 96% of survey respondents citing its current or potential value here, and almost half (45%) describing AI as “very useful”.

By use case, almost all respondents could see direct potential for AI in addressing identified pain points. The most popular proposed use cases included transforming labelling compliance and deviations maintenance; capturing, searching, filtering the latest regulatory requirements; automating the intake of Health Authority interactions; automating regulated content translations for different markets; automating the authoring of responses to Health Authority queries; suggesting improvements to submissions/dossiers; performing regulatory impact assessments; authoring submission documents; automating document summarization; and generating entire regulatory submissions.

What’s more, over a third (35%) of respondents claimed to be using AI for regulatory purposes in some form already, while 42% plan to invest in the next 18 months. A further 15% are looking at a timeframe beyond that, but also have plans to roll out AI within the regulatory function.

Inertia is still evident

Inertia is still evident, however, and this could be impeding companies’ ambitions for process transformation. Asked what, if anything, was inhibiting investment in AI for Regulatory purposes, respondents most commonly cited outdated existing IT landscapes (45%); a belief that risks currently outweigh the benefits (44%); and inadequate availability/quality/consistency of data or content resources to derive the value from AI (42%).

In addition, 39% of respondents felt the technology remained too immature/unproven; similarly, that the tools do not exist today to address their particular regulatory pain points. Sixteen per cent blamed a lack of trust in AI currently. This was ahead of budget challenges: only 15% named a lack of budget as a barrier to AI investment.

Drivers of change

Asked what would prompt new or additional investment in regulatory-focused AI capabilities now or in the near future, respondents indicated the discovery that their competitors are using the technology (41%); unsustainable further rises in workloads or resource pressures (40%); a maturing of the technology (36%); the availability of specific tools geared to the tasks regulatory teams find most challenging or expensive (35%); and relevant IT systems becoming easier and more affordable to deploy (33%).

Beyond those drivers, 31% said updating their upgrades to existing IT set-ups (making it possible to use AI reliably) would prompt investment. Endorsement or recommendation of AI by regulators would inspire investment also for just under a third of respondents.

By contrast, budgets do not appear to be a barrier to investment plans: just 18% indicated that the availability of new budget would unlock AI investment. This is encouraging, since hesitancy linked to “a lack of confidence to deploy” is readily addressable today. AI technology, including Generative AI (GenAI) is advancing at an accelerating pace, and specialist applications for target use cases in a life sciences regulatory context are being actively piloted today, showcasing what is possible.

Looking ahead, the survey asked for respondents’ expectations of AI in a regulatory environment over the longer term. Almost half (48%) of respondents agreed that, in due course, AI would transform a lot of routine regulatory work and considerably streamline processes. Over 2 in 5 (43%) felt AI would drive up accuracy and quality in the information they produce for regulators and patients. Almost 2 in 5 (39%) respondents believed AI would be critical to the regulatory function’s ability to keep pace with market demands. And over a third (35%) of respondents agreed that AI would save considerable time and money.

Taking action

Finding a targeted use case to test what the technology can do is a practical way forward for companies keen to move forward now and gain a process advantage. Taking an agile, incremental, use-case-by-use-case approach to GenAI deployment will be faster, and represent lower cost and lower risk, than a big-bang “AI project”. It is also more likely to build engagement, as specific incremental wins are demonstrated.

Whether companies are exploring AI in earnest for the first time, or looking to increase the technology’s usage and impact, however, they must first get their data in order and stabilize their core systems, so that they can then fully streamline and optimize their processes with targeted use of AI.

The full research report is available here https://www.arisglobal.com/resources/regulatory-industry-survey/

About the research

This report is based on an exclusive survey conducted for ArisGlobal by Censuswide between August 30 and September 06, 2024. The research was conducted among a sample of 100 US respondents with senior titles in regulatory roles within pharma/biopharma companies.

Pharma R&D latest disruption is via a data-driven, AI-enabled mode of drug discovery, evidenced by the rise of ‘AI-first’ companies such as Recursion and Insilico Medicine. Traditional pharmaceutical companies are also keenly incorporating AI across their businesses. Although the perceived value of AI in drug discovery and development has struggled to match the hype, a series of successful point solutions have emerged now which are having a tangible impact on particular steps in the R&D pipeline, giving rise to some useful takeaways about how to successfully embed AI into its core cycle.

Positive progress

In target discovery, knowledge graphs are adept at integrating a vast number of data sources into a query-able structure, which can be used to make informed and relatively unbiased target prioritization. A graph containing 84 million relationships derived from 37 separate sources has been successfully applied to the problem of target triage, identifying the most promising targets from hundreds of hits arising from genome-wide functional genomic CRISPR screens[1]. The time taken to identify hits for validation was reduced from months to days.

Challenges remain, however. Predicting synergistic drug combinations has been the topic of extensive research, and every flavor of AI model has been assessed with only limited success and almost no translational relevance. Nor are we any nearer to being able to predict the effect of a drug on a given patient without first running a clinical trial. Progress will require a structured and integrated approach to AI-enabled R&D transformation, spanning Data, Model, Culture and Validation considerations.

Data

Up to now, AI has found most success where the data set is large, complete and in many cases has been generated specifically to solve the problem at hand. The UNI foundation model for computational pathology, for instance, was trained on >100 million images from 20 tissue types.

In contrast one of the largest datasets available to train models for drug combination synergy prediction has 910 combinations of 118 drugs – many orders of magnitude smaller.

This problem is further exacerbated when we look at data from clinical trial cohorts, which is often sparse, and inconsistent in what is measured. For example, one trial might collect demographics and data for a specific blood-based biomarker; another might also collect genomic data.

Then there are differences in the analysis pipelines applied to all these data. Re-processing and harmonizing all of these data types is highly labor intensive, and often only the start of the process. The features used to train models may not be derived directly from the harmonized data and may need significant further manipulation.

The underlying issue, is that Pharma’s data, particularly that from clinical trials, was not generated for AI. To exploit data in a meaningful way using AI, companies must develop a data strategy – and be willing to fund and generate data on clinical cohorts if possible – to build useful data of the required scale.

Model

While AI models excel at classification and predictive problems, if AI is to revolutionize drug discovery it must incorporate causality. Predicting that a drug might work in a new indication is valuable, but it is not the same as explaining why the drug will work in that indication. To support internal and regulatory decision-making it is essential to have explainable biology that supports a mechanistic understanding of the particular drug or biology.

The integration of prior knowledge and data-driven insights offers a promising solution. AI combined with highly accurate causal relationships can distil both a broader array of targets with strong promise, and a mechanistic understanding of their biological role in disease.

Cause-and-effect relationships can be mined from the literature and created from experimental data. These relationships, defining the regulatory interactions between two biological entities, can be combined into structural causal models – a framework to represent and analyze the causal relationships between variables. Such models provide a systematic way to model how changes in one variable can lead to changes in another. These could be used during the training process of more expansive foundation models, but also to build specific mechanistic models that further describe the output from an upstream finding.

Validation

The output from all AI solutions should be validated, experimentally if appropriate, with two provisos. First, the R&D function should be set up so that all data feeds back to the AI model. This helps to mitigate some of the challenges described above, while ensuring that the model can be continually improved. For example, every result from the CRISPR screening group should find its way back into the knowledge graph so that future queries can benefit from that data.

Second, there needs to be a triage-based validation model. While an AI system is able to identify hundreds of targets, the challenge is to stay open to ‘left-field’ opportunities that AI might highlight. Orthogonal in silico approaches might be used to go from 1000 to 100 targets, but to go from 100 to 10 the team should adopt the quickest, most high-throughput experiment to yield the next rung of supporting evidence.

Culture

Underlying many of the data, model and validation issues up to now has been the culture of the organization and its failure to fully adapt to an AI driven way of thinking or working.

While there are increasing efforts to bridge this gap, upskilling or recruiting talent with AI expertise is essential. At the same time data scientists must be educated in the decision-making process of R&D, and understand/develop methods that directly support that. More could also be done to build the understanding that AI will raise the productivity level of all R&D researchers, and is therefore an opportunity and not a threat.

Facilitating change

Within 5-10 years, every major decision taken along the drug R&D pipeline will be accelerated by unparalleled access to knowledge. But to get there, data scientists will need to develop actionable models with causality at their heart; biologists will need to determine how to effectively integrate data science into their workflows; and heads of R&D will need to orchestrate more seamless integration and symbiosis between the two sciences.

The global medical device industry is expected to be worth $886.80 billion by 2032[1]. It is no coincidence that company representatives now make up a growing proportion of attendees at Regulatory Affairs Professionals Society meetings. As devices become more critical to patient outcomes, regulators around the world are steadily increasing their associated safety controls.

A new international benchmark report[2] considers how well manufacturers and their international partners are adapting to the rising regulatory demands. The 2024 study was conducted with 202 regulatory professionals at Class 2 and 3 device companies in the EU (Germany) and North America (the US).

Emerging & intensifying regulatory requirements

The research first tested medical device companies’ awareness of and current involvement with a number of increasingly prominent regulatory initiatives.

E-labeling/eIFU

E-labeling is high on the medical device regulation agenda on both sides of the Atlantic. Electronic information provision and management promotes standardization and consistency (e.g. of format and terminology), making it easier to manage and process the contents in any market. It also plays a key role in product traceability, a critical safety lever.

Providing critical product information digitally (e.g. under expectations associated with electronic instructions for use, or eIFU) makes it easier to issue prompt updates, too. It also simplifies international content and translation management and, in the case of user advice or safety information, facilitates spontaneous online or mobile lookup by clinicians or patients. Crucially, e-labeling allows device manufacturers to provide more detail than can fit on a physical label.

Currently, just under two-thirds (62%) of medical device companies are involved in e-labeling initiatives. EU companies are more likely to be actively involved in e-labeling than those in the US (71% vs 53%, respectively). This makes sense as the EU is ahead of the US with the practice; companies here are also less likely to outsource labeling as a service.

FHIR/standardized data exchange

Fast Healthcare Interoperability Resources is a proposed new global standard, designed to streamline data exchange and facilitate real-time information access for healthcare providers. It will shift the emphasis of content creation and management to ‘publishing’ rather than ‘printing’, promoting the digitization of the production and management of regulated medical device information and content.

In the survey, three in five respondents (60%) were involved with the standard, rising to 67% for EU (German) respondents; in the US, only just over half were occupied with FHIR (FHIR is not as high profile in the US), though the FDA is encouraging manufacturers to adopt interoperability standards.

UDI/device identification

Unique device identification (UDI) employs a unique numeric or alphanumeric code to identify individual devices across the healthcare supply chain. Although approached slightly differently, a UDI system is advocated by both EMA and the FDA. Benefits include expedited and more targeted product recalls, a reduction in product counterfeiting, and a better, safer experience for patients.

In the survey, two-thirds (66%) of respondents (rising to 74% of EU survey participants, but accounting for a much lower proportion in the US at 57%) express involvement in UDI activity.

Navigating new demands: upcoming priorities

So how are medical device suppliers responding to growing authority expectations?

Compared to the pharmaceutical market, the use of regulatory information management (RIM) systems is currently less prominent in medical device companies, featuring for just 29% of respondents, followed by structured authoring/creation tools (27%). The penetration of formal systems in the medical device sector is likely to grow as ambitions rise and regulations expand.

Respondents indicated that as they continued to invest in existing devices, as well as projects linked to emerging healthcare trends, and new materials and technologies, they would look to a range of technology solutions for support – most notably electronic document management (EDM); content management; proofreading/content comparison; labeling management; and product lifecycle management solutions – each cited by a third of respondents.

To cope with increased regulatory submissions, more than a third (36%) of medical device companies use software to accelerate the proofreading and content review process for regulatory documents, labeling materials, and promotional content; while a slightly smaller proportion (29%) still do this manually in house – rising to 37% of medical device companies in the US. In the EU, more respondents (41%) use software to help them review content quality. A third (34%) of all respondents outsource content proofreading, which could be as part of a broader arrangement with an external partner.

As tracking and supply chain transparency requirements rise, the challenges of producing compliant and correct device packaging and labeling for each respective market intensify. In the research, this pain point saw particularly strong responses. Just under two thirds (65%) of respondents said they find translations challenging to manage; 61% find barcodes challenging to manage; 60% struggle with graphics including symbols (shorthand guidance on device sterilization, for instance); and 59% have difficulty with tables. This is on top of any issues getting the text right (cited as a challenge by 54% of respondents).

Technology could offer a powerful solution here, although enhancements to processes are essential too to maximize any investment.

Lessons learned & next steps

The study culminated with device companies’ top takeaways that will inform their next regulatory actions. These were greater investment in company culture (cited by 35%, rising to 43% of German/EU respondents); increased resources/recruitment (34%); greater emphasis on wellbeing (33%); more investment in technology (33% – rising to 42% of US respondents); and increased focus on education and training (32%).

The prioritization of cultural and wellbeing factors further highlights the growing pressure regulatory functions are under, and the criticality of making teams – and the way they work – part of the solution.

[1] Medical Devices Market, Fortune Business Insights, June 17, 2024: https://www.fortunebusinessinsights.com/industry-reports/medical-devices-market-100085

[2] The independent Censuswide survey, commissioned by Schlafender Hase, was conducted in late May/early June 2024, among 202 regulatory professionals at Class 2 and 3 medical device companies (those deemed of intermediate to high risk in the event of a malfunction or quality/safety issue). The samples were split 50/50 between respondents in the EU (Germany) and North America (the US).

Over the last 30 years the gene therapy community has been constructing its map. In September 1990 W. French Anderson and colleagues at the US National Institutes of Health (NIH) performed the first gene therapy procedure on a 4-year-old girl (Ashanti Desilva) born with severe combined immunodeficiency (SCID).

Despite a few horror stories reported in the popular press, the trial was a success and Ashanti is still alive.^1,2 In 2017 and 2019 we witnessed the first two gene therapies approved by the US Food and Drug Administration (FDA) for commercial use: LUXTURNA, for inherited retinal blindness due to mutation of the RPE65 gene, and ZOLGENSMA, for spinal muscular atrophy (SMA) in patients less than 2 years old.^3,4 In addition, over the last 2 years we have seen approval of three other products by either the U.S. Food and Drug Administration (FDA) or European Medicines Agency (EMA) or both, including ZYNTEGLO for adult and pediatric patients with β-thalassemia; SKYSONA for cerebral adrenoleukodystrophy; and LIBMELDY for metachromatic leukodystrophy in the European Union, with another 5 approvals expected in 2023.⁵  In addition to these approvals is a healthy pipeline of gene therapies in the clinical trial setting, including over 376 active trials.⁶

But the gene therapy map is not all peaks; there have been some valleys as well. In 1999 Jesse Gelsinger died during a gene therapy for ornithine transcarbamylase deficiency when he had a fatal immunologic response to an adenovirus vector.⁷ More recently, we have seen additional high-profile safety issues, including deaths associated with AAV vector gene therapies that have raised safety concerns among the public and regulators.^8,9 We have also recently seen a 70% reduction in follow-on funding across cell and gene therapy companies that have impacted how they are approaching development, especially regarding manufacturing.⁵ There are looming questions about how health systems will afford gene therapy as a modality when there are 50 gene therapies on the market or when these therapies are developed for more prevalent diseases. The objective of this article is to look at future directions for gene therapies by describing three future trends and how they might impact safety, manufacturing development, and reimbursement and payment.

Trend 1: Enhanced and More Specific Vectors and Delivery Mechanisms:

Generally, the gene therapy delivery mechanism has been shown to be safe; however, there have been a few fatal cases of adverse events associated with adeno-associated viruses (AAV) viral vectors. To proactively confront some of the safety issues we are and will be seeing the advancements in virus vector design, new delivery mechanism and nonviral vector development.

Tissue-Specific Promoters for AAV Vectors:
For a gene therapy to work there needs to be a therapeutic gene and polyadenylation signal. The promotor’s job is to control the gene expression.¹⁰ The traditional promotors have not been tissue specific sometimes leading to low expression by silencing the transgene or overexpression resulting in cell damage and toxicity. One of the trends we see in the future is a movement towards using tissue-specific promotors. Tissue specific promoters are promoters that only work in specific cell types and can minimize over- and under expression.

Better Device Delivery Mechanisms:

Another trend we predict is the use of new devices to deliver more vector in a more localized manner to increase efficacy and minimize toxicity. We are starting to see this trend especially in central nervous system (CNS) disorders.  For example, many gene therapies targeting the CNS deliver the vector through a bolus injection into the lumbar spine often with minimal brain penetration and cellular targeting. New approaches being developed uses computer models to understand the cerebral spinal flow dynamics in an individual to deliver vector more precisely.¹¹

Non-Viral Vectors:

According to a recent analyst, more than 92% of vectors used for gene therapy are viral. With that said, we are seeing a growing interest in using nonviral vectors, especially for therapies requiring larger payloads and as a mechanism to immunogenicity. There are 2 main ways nonviral vectors deliver material: physically and chemically. Physical methods allow researchers to directly deliver genetic material to target cells. Ex vivo electroporation is an example. Chemical methods use natural or synthetic materials that are compatible in vivo and include lipids, polymers, nanoparticles, and ministring DNA.Presently, there are 60 therapies in the pipeline exploring the use of nonviral vectors.⁶

Trend 2: A Phased Approach to Manufacturing:

As mentioned earlier, follow-on funding for gene therapy companies has declined, putting more financial pressure on companies. As a result, we are observing more companies taking a phased approached to a manufacturing build out, helping to extend capital. The typical phased approach starts with limited capital investment for non-Good Manufacturing Practice (GMP) operations, including process development and analytical development (PD/AD), then grows incrementally with small-scale GMP capabilities until enough clinical data are available to drive funding and further large-scale investment.

When companies make large-scale investment and either build or enter a long-term lease, we see the best management of downside for those that position their facility as a flexible asset. Flexibility is an important element in manufacturing design in that it can accommodate multiple modalities and scales. A flexible design can mitigate the risk of a modified platform within one’s company, as well as position the facility as an asset. If a facility has flexible design, with modular walls and acceptable ceiling clearance, then the facility becomes a strong asset to accept a modified platform, or worst case, repositioning to the market.

Trend 3: Cost-Effectiveness vs. Cost-Effectiveness and Affordability:

Cost-Effectiveness: To date most of the gene therapies on the market or approaching approval have been for relatively rare diseases with very high unmet clinical need. This presents unique challenges to payers:

• Limited data: Often small, single-arm trials due to rareness and/or the transformational impact do not provide wide-reaching results
• High price points: The promise of long-term patient benefits means that these therapies can be cost-effective or even cost-saving at very high price points, putting them among the highest priced pharmaceutical options
• Upfront cost: Single dosing may mean that payers need to pay the entire cost of treatment up front and in advance of knowing the full longevity of effect

Despite some of these challenges, payers have been covering and paying for gene therapies based on the value they have been demonstrating in cost-effectiveness models and the limited budgetary exposure.

Cost-Effectiveness and Affordability:
The gene therapy pipeline is large. Currently there are 376 active gene therapy trials with 59 of those in Phase 3. Of the Phase 3 trials, 40 of them are for rare disorders, whereas 19 are for more prevalent disorders.⁴ With the potential for many more gene therapies on the market over the next 3 to 5 years and some of them for more common disorders, payers will become much more concerned about the overall budget impact.

We are starting to see this trend both in the United States and in Europe. In the United States we have observed the emergence of gene therapy “carve outs.” For example, Cigna, eviCore, Accredo, and Express Scripts have created a gene therapy “carve out” aimed at the self-funded, self-insured employer population, in which the employer pays $0.99 per member per month to insure that individual for gene therapy.¹² This reduces the financial risk to the employer, but we have yet to see how these carve outs will impact patient access. In Europe some countries are employing dynamic approval models, in which conditional approvals at a price are given and then reevaluated once additional clinical trial and/or real-world data are available.¹³ For example, in Germany the G-BA may apply a time limit to the benefit assessment granted, meaning that that the product must undergo another benefit assessment (and rebate price negotiation).¹⁴ The gene therapies Libmeldy and Luxturna were both subject to time-limited benefit resolutions in Germany.

Parting Thoughts

As these trends illustrate, gene therapies are evolving across every aspect of the development cycle. There are now “more places that we might go.” The potential impact of gene therapy is fundamentally so profound that we must take this journey.

References

1. Anderson WF. September 14, 1990: the beginning. Hum Gene Ther. 1990;1:371-372.

2. Springen K. Using genes as medicine. Newsweek, December 6, 2004. https://www.newsweek.com/using-genes-medicine-123277.

3. FDA News Release. FDA approves novel gene therapy to treat patients with a rare form of inherited vision loss. Accessed November 11, 2022. https://www.fda.gov/news-events/press-announcements/fda-approves-novel-gene-therapy-treat-patients-rare-form-inherited-vision-loss.

4. FDA News Release. FDA approves innovative gene therapy to treat pediatric patients with spinal muscular atrophy, a rare disease and leading genetic cause of infant mortality. Accessed November 11, 2022. https://www.fda.gov/news-events/press-announcements/fda-approves-innovative-gene-therapy-treat-pediatric-patients-spinal-muscular-atrophy-rare-disease.

5. Alliance for Regenerative Medicine. Regenerative Medicine: The Pipeline Momentum Builds H1 2022 Report. Accessed November 12, 2022. https://alliancerm.org/sector-report/h1-2022-report/.

6. Watt A. A gene therapy landscape analysis of rare vs. common disorders. Gene Therapy for Rare Disorders Conference. 2022. Boston, MA.

7. Gene-therapy trials must proceed with caution. Nature. 2016; 534:590. https://doi.org/10.1038/534590a.

8. Masson G. Another Pfizer gene therapy is free of FDA hold, but delay continues. Fierce Biotech. 2022. Accessed November 12, 2022. https://www.fiercebiotech.com/biotech/fda-frees-pfizers-hemophilia-gene-therapy-clinical-hold.

9. Keown A. Another patient dies following treatment with Astellas’ experimental gene therapy. Biospace. Accessed November 12, 2022. https://www.biospace.com/article/patient-dies-following-treatment-with-astellas-pharma-s-experimental-gene-therapy/.

10. Zheng C, Baum BJ. Evaluation of promoters for use in tissue-specific gene delivery. Methods Mol Biol. 2008; 434:205-19.

11. Alcyone Therapeutics. Accessed November 16, 2000. https://alcyonetx.com/science-technology/falcon/.

12. Embarc Benefit Protection Plan. Accessed November 16, 2022. https://www.cigna.com/employers/cost-control/embarc-benefit-protection.

13. National Institute for Health and Care Excellence. Managed Access. Accessed November 29, 2022 https://www.nice.org.uk/about/what-we-do/our-programmes/managed-access

14. Macaulay R, Wang G, Majeed B. Time-limited G-BA Resolutions – A tool to Appropriately manage reimbursement of innovative therapies receiving expedited regulatory approval. European International Society of Pharmacoeconomics and Outcomes Research Conference, (2018). Barcelona, Spain.

Many clinical research organizations set the goal of putting patients first, but find the achievement elusive. Patient-centricity is a measurable goal that is a prerequisite for organizations aiming to make clinical trials more accessible and convenient for study participants. Consider the following steps to create a meaningful patient centricity initiative.

Let Data Drive Patient Centricity

The key to implementing a successful patient centric strategy is a data-driven decision process designed to enrich the patient journey during clinical trials. Ensure your patient engagement team is constantly assessing, improving, monitoring, and reporting on the end-to-end patient experience. The first step is analyzing the patient journey by communicating directly with trial participants.

Create a Useful Dashboard

Gather a cross departmental team of key internal and external stakeholders and conduct a SWOT (strengths, weaknesses, opportunities, and threats) analysis. Assign a team to assess, improve, monitor and report on the end-to-end patient experience in a consistent way across the many touch points between patients and your site’s clinical trials. The dashboard can be used to assess patients’ experiences from when they first become interested, to when they are screened and scheduled, when they are randomized, and, finally, when patients complete the study.

Use your dashboard to create subcategories for each of the patient touch points mentioned above. For example, from the way a patient was medically screened for the study, to the consenting process, to the way they are treated at the clinic, all of which influence the patient experience.

Survey Your Patients Repeatedly

Conduct multiple surveys, including following the first patient visit, mid-study visit, study completion, and initial trial phone screening. Your team may be surprised how much room there is for improvement in even the most basic interactions, such as responses to patient calls and messages.

Track Problems & Respond Quickly

Establish a customer support team that tracks issues and concerns from patients in real time through the patient dashboard. We established a customer support team that manned the phone line and tracked issues and concerns from patients in real time on our dashboard. The feedback ranged from transportation to the clinics, trial compensation, and communication with staff, as well as electronic diary and compliance issues. Our tracker also informed us what clinical trial site and how often that issue occurred at that location in order for us to identify trends and monitor risks.

The second part of our dashboard used survey data to gather feedback through different points of the patient journey. We did a combination of automated surveys and spot individual interviews. This allowed us to have unfiltered contact with patients that were enrolled or considering enrolling. Their insights helped us shift the culture within the organization across multiple departments. With clinical trials becoming more challenging to recruit for, we wanted to make sure we did our part in measuring progress through data to continuously improve processes. A mixture of qualitative and quantitative data helped change perspectives around the need for a more patient-centric culture.

Create A Patient Advocate Program

Create a specialized Patient Advocate training program and set up dedicated resources at each study site to help enrich the patient experience. We created scorecards for each site that measured things such as Google reviews, issues noted on the dashboard tracker, focus group data, and survey data. The data allowed us to develop strategies for each issue through open dialogue in a shared decision making approach.

Our Patient Engagement team acts as independent auditors and mentors for the company ensuring that there is a team focused solely on the quality of the patient experience—very similar to how the Quality Assurance team helps safeguard data quality. The dashboard allowed us to proactively plan for patient engagement because the idea was to change internal behaviors to accept that patient engagement was a company standard and expectation.

Making Patients Part of the Clinical Process

A more patient-centric approach to clinical research allows organizations to identify common trends and unique situations that impact the way participants perceive research and medicine in general. Changing organizational culture to be more patient-centric requires reliable metrics and Key Performance Indicators (KPIs).

Using the statistics obtained from patients across trial sites, organizations can allow study participants to play an active role in customer service and clinical decision-making within the research network. This provides nothing less than the best possible association of the patient’s clinical research experience with the pivotal goal of aiding in discrediting the negative stigma associated with the clinical research industry in the public eye.

What Are AAV Vectors?

Wild-type AAVs are some of smallest viruses (approximately 25 nm) with a linear single stranded DNA genome of 4.7 Kb in length with 2,145 nucleotide inverted terminal repeats. The linear DNA has 3 genes encapsulated in 60 outer coat capsid, which include rep (replication) gene encoding 4 proteins required for viral genome replication and packaging, cap (capsid) that encodes the proteins in the capsid, and aap (assembly activation protein) that provides the structure for the capsid.^1-3 In order to make AAV viruses usable for gene therapies, they are engineered into recombinant AAVs (rAAVs), where the viral genome is replaced with a promoter, the gene or genes of interest, and a terminator.⁴ Recombinate AAVs cannot replicate in vivo and need a helper virus to replicate and are not passed on during cell division. These properties make them very safe vehicles to drive long-term gene expression after a single infection.^5,6 Presently, there are 11 serotypes (AAV1 through AAV11) cloned, and they can have specific affinity to tissues. For example, AAV1, AAV2, AAV4, AAV5, AAV8, and AAV9 are optimal for central nervous system (CNS) disorders.⁷

What Are Major Considerations When Selecting an AAV Vector?

There are 3 major considerations when selecting the appropriate AAV:

1- Selecting the right capsid and promoter: Selection can improve targeting of cell transduction and increase expression, which has implications for the dosage required for effective therapy.

2- Selecting the best dosing regimen: The choice of dosage can lead to either reduced efficacy when the doses are too low or can lead to toxicities when the dosing is too high. Dosages that are too low could lead to inefficient transduction; dosages that are too high can result in delivery and transduction-related toxicities.8 Inflammatory toxicities have been seen with increasing doses of gene therapies resulting in complement activation, cytopenia, and severe hepatotoxicity. Therefore, it is ideal and safest to have as minimal dose as possible while still being clinically effective. Majority of the deaths associated with AAV vector have been seen at higher doses.                

3- Development of immune responses to the vector (immunogenicity): Many individuals have already been exposed to one or more serotypes of wild type AAV, and thus may have some degree of pre-existing immunity to the vectors used that includes binding antibodies and neutralizing antibodies (NAbs), which may negatively impact clinical efficacy and could increase post-therapy prevent re-administration. The prevalence of pre-existing anti-capsid NAbs varies by AAV serotypes, ranging from approximately 40% for AAV8 to 74% for AAV2.9

Strategies for Mitigating Safety Issues with AAV Vectors

Presently, there are multiple strategies being deployed or developed to mitigate risk associated with AAV vector usage and to increase performance, including excluding individuals with some level of existing NAbs, depleting NAbs, vector engineering removing immunogenic features during vector design lowering therapeutic dosing, and immunosuppression.  

Excluding Patients with Naturalizing Antibodies (Nabs):

Often patients with pre-existing Nabs against the specific AAV capsid are precluded from receiving the gene therapy. This occurs often in clinical trials with 45% excluding patients with a pre-specified Nab levels. With that said, this exclusion in clinical trials is quite variable among therapeutic areas, where approximately 90% of the blood disorder trials exclude patients, and ophthalmologic and CNS clinical trials excluding 10% and 21%, respectively. This represents the current hypothesis that NAbs are of greater concern for systemic delivery of AAV than for targeted therapy to immune privileged sites.⁵

Tissue-Specific Promoters for AAV Vectors:

For a gene therapy to work, there needs to be a therapeutic gene, promotor, and polyadenylation signal. The promotor’s job is to control the gene expression.¹⁰ The traditional promotors have not been tissue-specific, sometimes leading to low expression by silencing the transgene or overexpression, resulting in cell damage and toxicity. One of the trends we anticipate is a movement towards using tissue-specific promotors. Tissue-specific promoters work only on specific cell types and can minimize over- and under expression.

Transgene Optimization:
Modifying the transgene to produce more-effective therapeutic

Proteins is another method beginning to be employed, with the hope that greater therapeutic efficacy will lead to lower doses and better safety with similar efficacy. An example of this is FIX-Padua, a variant of FIX (Factor “9”) with a hyperactivating R338L mutation, which resulted in increasing the efficacy by up to 5–10-fold in hemophilia B patients at lower doses with no liver enzyme elevation in the majority of the patients.¹⁰

Better Device Delivery Mechanisms:
Another strategy being employed is the development and use of new devices to deliver more vectors in a more localized manner to increase efficacy and minimize toxicity. We are starting to see this trend, especially in CNS disorders. For example, many gene therapies targeting the CNS, delivering the vector through a bolus injection into the lumbar spine, often with minimal brain penetration and cellular targeting. New approaches being developed use computer models to understand the cerebral spinal flow dynamics in an individual to deliver vector more precisely.¹¹

Immunosuppression:
It is becoming more common to administer immunosuppressive agents as part of a AAV gene therapy. In the first few AAV gene therapies trials, corticosteroids were used reactively after the therapy administration when liver enzymes became elevated. The introduction of the immunosuppressants usually corrected the elevated enzymes. Recently, it has become prevalent to administer immunosuppressants proactively. Common immunosuppressants used are corticosteroids, rapamycin, tacrolimus, mycophenolate, rituximab, eculizumab, and hydroxychloroquine. All of these immunosuppression agents can have significant side effects, so it is important to factor in the safety profile of the immunosuppressant in conjunction with patient clinical presentation before administering.¹²

FDA on Safety Issues with AAV Vectors

In May of 2020, the US Food and Drug Administration (FDA) issued general guidance for the industry on human gene therapy (GT) for rare diseases. In that guidance, the FDA notes that “pre-existing antibodies to any component of the GT product may pose a potential risk to patient safety and limit its therapeutic potential.” In addition, antibodies to the gene therapy may also limit the re-administration of the therapy to a one-time use. The FDA also stated “Sponsors may choose to exclude patients with pre-existing antibodies to the GT product.” If patients are excluded based on pre-existing antibodies, the sponsor should strongly consider development of a companion diagnostic to detect antibodies to the to the gene therapy. If an in vitro companion diagnostic is needed to appropriately select patients for the clinical study, then there should be coordination of the companion diagnostic marketing application with the biologic license application for the gene therapy.¹³

In response to a few high-profile adverse events seen in clinical trials and surveillance of AAV gene therapies, on September 2^nd and 3^rd of 2021, the FDA’s Cellular, Tissue, and Gene Therapies Advisory Committee held a meeting to discuss toxicity seen with AAV vectors. The meeting centered on the following adverse events: hepatotoxicity, thrombotic microangiopathy (TMA), dorsal root ganglion (DRG) toxicities, neurotoxicity – MRI findings, and oncogenicity. The FDA questions to the committee centered around:

Hepatotoxicity: (1) Role of animal studies? (2) Before AAV administrations, how should patients be screened and categorized for risk of liver injury? (3) What strategies could be implemented to prevent or mitigate the risk of liver injury? (4) Beyond weight of the patient, what factors (e.g., level of disease severity) should be considered to determine the vector dose for systemic administration? (5) Considering the risk of toxicities observed in clinical trials with high doses of AAV vectors, should an upper limit be set for the total vector genome dose per subject?

Thrombotic Microangiopathy (TMA): (1) What factors may increase the risk of TMA following AAV vector administration? (2) What strategies could be implemented to prevent or mitigate the risk of AAV vector-mediated TMA? (3) Should an upper limit be set for the total vector dose? (4) Discuss whether an upper limit should be set on the total capsid dose.

Dorsal Root Ganglion (DRG) Toxicities: (1) based on the published data, please discuss the relevance of the non-human primate (NHP) cases of DRG toxicity to human subjects. (2) Please provide recommendations on preclinical study design elements, such as animal species/disease model, age, in-life and post-mortem assessments, and duration of follow-up, post-dose that may contribute to further characterization of DRG toxicity. (3) In addition to periodic neurological examinations, please provide recommendations on other methods to mitigate the risk of DRG toxicity in clinical trials.

Oncogenicity: (1) Discuss the merits and limitations of animal studies to characterize the risk of AAV vector-mediated oncogenicity. (2) Discuss benefit-risk considerations for AAV vector-mediated oncogenesis, such as patient age at the time of treatment, pre-existing liver conditions (e.g., infection with hepatitis B or C virus), and high vector dose.

Although there has not yet been a formal guidance issued from this meeting, the meeting did serve to help define some of the major questions regarding AAV vectors.¹⁴

Parting Thoughts The final comments from Peter Marks MD (US FDA) frame the situation well: “I would say the fact that we’re discussing this is evidence that they (gene therapies) very much are becoming a reality. And it’s actually a good sign because with every medical therapy, as it comes along, we have to deal with the side effects that may come up and address them.”¹⁴ As Hagrid said to Harry Potter, not all wizards are good. Well, not all AAV gene therapies are bad. AAV gene therapies hold tremendous promise, and we are engaged in the iterative process of refining AAV therapies. We are turning magic into science!

Since the dawn of the trade, pharma’s mission has been to improve health outcomes for patients. For many of us, it’s the ultimate measure of success, more so than any bottom line in a ledger book. But as time progressed, we were relegated mostly to a process in the background, charged primarily with developing and manufacturing drugs. That “firewall” has taken us away from our roots: the one-on-one interactions with our patients. We’ve become a cog in the machinery rather than a trusted partner in the patient’s health journey.

This article outlines what needs to be done to bring the pharma industry and its patients back together. It will reveal how we can engage with patients better and deliver the experiences they expect.

Patient Engagement: A Far Cry

Across many patient surveys, pharma has consistently ranked poorly in terms of engagement and patient experience across all touchpoints and channels. Most patients don’t even remember the pharma company involved in their treatment journey. For years, our industry has agreed that engagement with the patient is essential, whether we called it “patient engagement,” “patient-centricity,” or something else. And to their credit, many companies have tried, but they find themselves with mostly siloed approaches. The holistic patient experience seems something too far out of reach. The answer is to look back to our history, which can teach us about engaging with our most important customers.

The original pharmacists in the apothecaries of Baghdad weren’t hidden in a room mixing concoctions. They were with their patients. They were holding their hands. They were observing what needed to be done and learning as they did it. Even with limited tools and understanding by our standards, they knew that being physically present, one-on-one with the patient, was a key ingredient to improving their patient’s health.

Fast forward a few centuries. As we entered industrialization, and chemists found compounds that could solve the biggest ailments plaguing mankind, a race for mass production started. With it came a rush— intentionally or not—for the most profit. That simple evolution led to a heartbreaking unintentional flaw: our purpose to serve patients became secondary. Sure, medical innovations helped double global life expectancy in the past century.¹ But they also pushed us further behind the firewall of partners, including hospitals and providers, government institutions, payers, pharmacies, and distributors. With every step in our evolution, we’ve fallen behind, and our partners have taken the front-row seat in terms of patient access and patient engagement. We are so far removed that many in the industry do not understand what patients expect from us.

Patient Journey and Siloed “Omni” Engagement

So far, we’ve identified the problem: we need to restore those one-on-one, direct interactions with the patients. We are approaching this today by employing an Omni engagement strategy, talking with the patients across several points in their journey. But we’re failing since we are not doing it cohesively.

Each function within pharma is trying to engage with the patient in a siloed process. For example, the commercial role of pharma mainly just involves access to therapies, education, and patient support. Each company function may have a patient profile, but it’s only that function’s view. They don’t venture or think outside of their limited box.

On the other hand, patient needs and behaviors have evolved rapidly, and so have their expectations. They want more from pharma, and contrary to all negative sentiments around the industry, patients are happy and willing to work directly with us if we deliver on their expectations.

Patients demand personalized care and experiences. They want access to care and are heavily focused on prevention and early diagnoses. They want ongoing education and seamless coordination of care. They expect self-managed health options with the ability to change therapies automatically when they make lifestyle changes. They want us to respect their preferences and privacy, along with emotional support and physical comfort. And, most importantly, they expect therapies developed for them based on their biology. The question is – what do we need to do to be ready to meet these patient expectations?

Broadening Our View of the Patient Journey

Think back to those apothecary days. Knowledge was a trickle. Today, health data is more akin to a fire hose. And the data is much more expansive in the pre-diagnosis and post-treatment phases. However, our definition of the patient journey only looks at the timeframe when an individual gets sick. On the other hand, patients want us to add value beyond when they are sick, including before diagnosis and

after they are treated. Nevertheless, we are doing very little to get access to that pre-diagnosis and post-treatment data, and we need it to get closer to our patients.

Key Building Blocks for the Patient-first Approach

Rebuilding our one-on-one connections with the patients to deliver those expectations requires a reset. We need to rebuild the foundation, and we need building blocks. After we’ve reset and cleared the site (i.e., knowing what you need to give the patient), we can begin. That starts with the foundation. There are four building blocks, and digital technology and infrastructure is the foundational layer as it is the key enabler of these building blocks.

Building Block One: Organizational Design and Culture

The first building block is organizational design and culture. At this stage, we must examine how our companies are arranged and model them so that every aspect is working towards the same goal: a patient-first culture. That will innately help us understand the patients’ needs across the value chain. A proper design with the patient’s well-being at the center will lead to greater efficiency and better decision-making.

Pharma companies have an organizational design that includes Discovery, Clinical, Manufacturing and Supply Chain, Regulatory, Commercial, Market Access, and other functions. Where is the function focused on patients? Sure, companies have a range of “patient engagement professionals,” but they are all in silos with very focused mandates. We see Chief Patient Officers at many pharma companies, but the role is contained under specific functions, mostly within Discovery and Clinical. There is no one person responsible for patient experience across the pharma value chain.

This is one of the key aspects of getting back to proper patient engagement: pharma needs to empower its Chief Patient Officers so that they can ensure the best patient experience across every touchpoint.

That doesn’t just mean fostering a culture. Talk is good, but action is a must. Instead, these Chief Patient Officers need to operationalize a culture of “patient empathy” across their organization. Processes and systems need to be implemented to measure these roles with metrics like “Net Patient Score.” That will gauge how we are progressing in terms of patient engagement across those touchpoints.

Building Block Two: Patient Engagement Strategy

Once your organizational design and culture are in place, you’re off to the races with building your patient engagement strategy. At this stage, we must put the company in a mindset of co-creation with the patient instead of one-way drug development and outreach. We must be able to capture and activate data across the patient journey and touchpoints. Only then, we can leverage patient insights across the value chain from R&D to Sales.

The strategy must enable partnerships within the healthcare ecosystem to break down “the firewall.” We must know patients at all phases of their health cycle. We must interact with them not just when they’re sick but when they’re healthy and when they’re not patients in an exam room. When we understand our patients in this manner, we will help them immensely with preventative and personalized care. We will be able to provide them with a holistic experience and empower them. That adds remarkable value to their lives while, in turn, providing us with constantly refreshed data, which again, in turn, leads us to improve our offerings and better care for all.

Building Block Three: Partnerships to Drive Patient Engagement

We must work closely, both internally and externally, with other players in the healthcare space. Collaboration with other stakeholders in the healthcare ecosystem will help enable access to patients

and their data. These partnerships will enable us to establish a true mapping of the patient journey and their needs to provide them with personalized care and experiences.

But these partnerships must start at home. Pharma’s internal silos must be broken so an accurate mapping of the patient journey and their needs can be established. Accordingly, the experience can be orchestrated at every touchpoint.

Building Block Four: Enable Access to Data

Once we have the above building blocks in place, they can enable access to data across the holistic patient journey from pre-clinical to commercial and beyond, including patient lifestyle data. The “lifestyle data” aspect is critical. Traditionally, pharma has never considered accessing patient data outside of drug development and sales. As new players like tech and retail rapidly get a hold of lifestyle information, they have access to real-time health data that can be a gold mine for pharma as the industry tries to develop personalized therapies for complex and rare diseases. The good news is that there are already many partnerships forming in this space in an effort to get access to this data.

Proximity Matters

One key element of patient engagement is proximity. Even where we see some aspects of building blocks being put in place and access to data enabled for Omni patient engagement, the focus is only on indirect and digitally enabled access to patients. Creating a digital patient profile versus actually getting in front of them can provide a very different understanding of the customer. Doing so enhances our ability to deliver experience, but there is minimal conversation about restoring physical proximity to patients. This can be explained visually; consider the “patient engagement flywheel” below.

This flywheel can help us assess where we are in our patient engagement maturity so we can focus on the right initiatives based on our stage. A flywheel is a better representation than a ladder, because our industry is rapidly evolving, and patient needs and expectations are evolving even faster. We must keep innovating to keep the patient engagement momentum going. Even when we get to a breakthrough point, any slowdown and we risk going back.

Most pharma companies are in the “start” phase on this flywheel, though some have entered the “build” phase. But as you can see, they don’t truly get to the breakthrough moment until they can get direct access to patients and serve their needs one-on-one.

Conclusion: Operationalize Patient Empathy

At the highest level, we must operationalize patient empathy across the pharma business. As mentioned earlier, this involves elevating and empowering Chief Patient Officers. It requires patient proximity by eliminating the firewall so we can co-create with them. And we need to democratize patient insights with investments in digital capability and data so we understand their personalized needs. Finally, we must implement systems and processes to measure our patient engagement efforts with key performance indicators like “net patient score.”

The question we must answer is this – do we have the vision, investments, partnerships, and leadership to go directly to the patients? In doing so, we will get back to the same way we started as an industry when we were serving the patients one-on-one.

Reference:

1. Roser M, Ortiz-Ospina E, Ritchie H. Life expectancy [Internet]. Oxford: Our World in Data. 2013 [revised 2019 Oct; cited 25 Oct 2022]. Available from: https://ourworldindata.org/life- expectancy

Throughout the ongoing COVID-19 pandemic, media outlets have highlighted the immunocompromised population quite frequently. This population of people are increasingly vulnerable to the highly contagious virus. Patients that are considered immunocompromised or have a weakened immune system due to medications they are taking or illnesses that they are enduring, may not be protected from contracting COVID-19 and progressing hospitalization, for example, even if they are up to date on their vaccines. This vulnerable population is more likely to get very sick from the virus as well. For background, patients over the age of 65, or those that have, for example, diabetes, obesity, a lung condition, cancer, etc. are all considered immunocompromised and vulnerable.

A Novel, Safe Way to Ensure Protection

For context, according to Yale Medicine, it is estimated that about three percent of the United States population is considered moderately-to-severely immunocompromised, making them more at risk for serious illness if they contract COVID-19, or other viruses. It is imperative to consider prevention options for this vulnerable population—not only for COVID-19, but for cancer and other diseases as well.

That’s where monoclonal antibodies come in. The first monoclonal antibody was generated in 1975 and was first licensed in 1986. According to the National Library of Medicine, “The field of monoclonal antibody development represents a novel way in which to target specific mutations and defects in protein structure and expression in a wide range of diseases and conditions.” They work as both a therapeutic and a preventative and are produced in a laboratory to mimic the body’s natural immune response to a specific target, such as COVID-19.

Immunocompromised populations typically don’t generate an immune response to vaccines even after multiple shots and boosters. Therefore, they need other forms of protection from the virus. Monoclonal antibodies are highly effective and designed to block the virus from attaching to human cells, making it more difficult for the virus to reproduce and cause harm—they have neutralizing capabilities. Protection can be gained by using a long-acting monoclonal antibody treatment. Monoclonals can provide up to six months of protection and are safe and effective, whereas antivirals can target not only unhealthy cells, but healthy cells as well, and are not designed to eradicate the virus directly.

Using the Human Immune System to Fight Cancer

It is extremely important to only focus on treating unhealthy cells and to leave the healthy cells alone—especially when tending to cancer patients. Monoclonal antibodies are used to treat many cancer types. Patients can receive the antibodies through an infusion, which are used alone or in combination with other cancer treatments such as chemotherapy. Antibodies treat patients in a very specific and targeted manner. Targeted therapies are known for better efficacy and are associated with fewer side effects during treatment compared to traditional approaches and therapies that can cause lingering side effects, since they focus on a broader scope within the body.

Some monoclonal antibodies are even used for immunotherapy because they help turn the immune system against cancer. It is possible for monoclonal antibodies to mark cancer cells so that the immune system can better distinguish and destroy them. Antibodies that bring various tumor-killing cells (made from the human immune system) directly to the targeted cancer cells specifically, are associated with higher efficacy and lower toxicity compared to existing therapies. It is also possible to attach a chemotherapy drug, for example, to a monoclonal antibody, which are called antibody-drug conjugates. This approach avoids most healthy cells (the goal), while delivering the chemotherapy directly to the cancer cell. In short, treatment methods combined with monoclonal antibodies are used to kill the cancer cell directly. As a reminder, those that are undergoing cancer treatment are considered immunocompromised and vulnerable. Therefore, it is essential that patients are aware of the resources available to them as well as the advancements that have been made in therapeutics—these therapies save lives.

Ongoing Support for the Vulnerable Population is Needed

There are a variety of ways to support those who are immunocompromised, especially during the ongoing pandemic and even as it becomes an endemic. In addition to prioritizing the use of preventative treatments such as long-acting monoclonals, it is important that a plethora of information/education is available for immunocompromised individuals about the availability and access to monoclonal treatment options.

Physicians should also be aware of and educated on the benefits of monoclonal antibody treatments, with a specific emphasis on how the use of the treatments can decrease hospitalization rates, estimated to be 80-90% lower with antibodies, and ultimately reduce fatalities significantly.Antibody therapies for infectious diseases are a relatively new treatment modality overall, so education is needed to inform physicians of their effectiveness and high safety profile when used early in the treatment process. One could show the therapeutic’s impact on preventing progression to hospitalization as well its use as a pre-exposure prophylaxis (PrEP). Additionally, when dealing with future variants, monoclonal antibodies serve as a better therapeutic due to their ability to specifically attack the foundation of the virus, which makes them a future-proofed option. At least 10 million people in the United States alone are considered vulnerable. The use of antibody therapies will have a lasting, immeasurable impact around the world—this population needs our protection.

All traditional marketing models consider the customer journey to be pre-defined. They rely heavily on static and intricate customer journey maps that include multiple scenarios of a customer’s journey and assign corresponding actions to each possibility. Historically, marketing and brand leaders have used such models to define their marketing strategies. While such approaches were relatively more practical in the past, the advent of digital channels and changing customer behaviour have made it difficult to map a customer’s route options towards a purchase decision.

In today’s age, it is impossible to predict all action possibilities of each customer along their purchase journey. They can choose to interact with (or not) any channel at any time, leading to infinite possibilities of interactions leading up to the decision. Excessive digital information has reduced customers’ attention spans, making it even more difficult to penetrate their consciousness and thought processes.

How can a just-in-time customer-response model drive omnichannel Optimization?

Developing responsive models that respond quickly to customer actions is essential to driving omnichannel orchestration. Such models exist in many degrees of sophistication across life sciences companies without a universally prescribed blueprint. Based on the volume of customer data and other factors influencing the analytics capacity of organizations, such just-in-time algorithms can be straightforward, complex, business rules-driven, or AI/ML-informed. Different companies can choose the best-suited omnichannel program for their unique requirements.

Customer-response algorithms in any shape or form need to address one common objective – recommending the next best action (NBA) to follow with all possibilities of customer behavior. The two straightforward approaches to achieving this objective are:

1. Alerts and micro-journeys (NBA)
2. Dynamic scores and segments

1. Alerts and micro-journeys (NBA)

Alerts are triggers that can deliver quick action recommendations for immediate customer requirements without missing out on an opportunity. NBA alerts range from elementary rule-based processes, such as a notification to a Key Account Manager that a Medical Scientific Liaison just visited a targeted healthcare physician (HCP), to sophisticated machine learning algorithms that monitor complex patient data for signals that an HCP has recently diagnosed a patient with a rare disease.

Micro-journeys are short sequences that can pre-determine recommended responses to standard customer journey fragments, such as recruitment, reminders, and follow-up messages around a webinar.

It’s important not to over-use NBA to control every contact. Customers will take complex and unpredictable journeys that make it difficult or impossible to model and apply NBAs to every step. Furthermore, some channels, such as sales teams or specialist marketing partners, cannot execute the actions in the planned timeframes of the journey plans.

2. Dynamic scores and segments

Dynamic scores and segments can contribute to continuously optimizing each customer’s channel mix and messaging. These powerful analytics tools can help sales and marketing organizations understand customer behavior and preferences and use that information to rank-order various channels to interact with the customer segments. Here’s an example:

• An HCP can be assigned segments for attributes such as target tiers and adoption ladders. These segments infrequently refresh with changes in the sales data.
• The HCP is also given scores against appropriate engagement options, such as face-to-face (F2F) meetings, marketing emails, and other digital channels. These scores guide channel priority and frequently change based on daily interactions with the HCP. For instance, the F2F score will decrease drastically immediately after a sales rep visit. The digital channel scores will increase to emphasize follow-ups, such as relevant emails to build on the sales rep’s visit.
• As the HCP progresses through their journey, the synchronized changes in the dynamic scores will guide the sales and marketing teams on the best-suited promotional content and the interaction channel to eventually nurture the HCP into a customer.

The dynamic scores of various channels can augment the NBA-driven interaction with the customer. Some of these scores may not frequently change (customer target potential and attitudinal segment), but others might frequently change (even daily). As the customer progresses through their journey, these scores will change, guiding the sales and marketing organization on the timing and priority of messaging and continuously improving the channel mix for the customer over time.

The highlight of such algorithms is that they are effortless and efficient in guiding sales and marketing teams in the right direction, but without fully taking control of getting things done. The operators can also intervene with the algorithms’ outcomes and make preferred changes.

Read Axtria’s white paper on “Optimizing your biggest promotional channel — a responsive, data-driven approach to empower field teams” to learn more about this dynamic scoring methodology!

Putting it Together

Dynamic scores, segments, alerts, and micro-journeys can fit well in a federated control model when efficiently used in a well-coordinated omnichannel system. While dynamic scores can be controlled centrally and guide the expert operators on recommended actions, alerts can fulfill pre-defined and automated rule-based tasks.

Both these components need to integrate seamlessly into any organization’s omnichannel platform for well-coordinated recommendations along each customer’s journey. Efficiently managing customer data is of limited use if the omnichannel orchestration engine does not accommodate changing customer preferences, intelligence-driven responses, and a degree of autonomy for the sales and marketing operators. AI/ML-driven customer scoring and segmenting models can regulate channel mix decisions, while automation workflows can assist sales and marketing personnel to deliver the desired customer experience. Therefore, it is critical to ensure a steady algorithm production line, from customer evaluation to providing the message, to ensure a robust omnichannel orchestration framework.

Thrombosis is the second leading cause of death in patients with cancer, after the cancer itself. One in five cancer patients will develop a blood clot; in some of these cancer patients, that clot can prove to be fatal.

Cancer patients are four times more likely than people without cancer to develop a potentially fatal blood clot. Risk factors that increase the incidence of thrombosis include the type of cancer, the cancer treatment, the patient’s Body Mass Index (BMI), age, smoking history, lifestyle (active or sedentary), and more. For example, a patient with pancreatic cancer is at a greater risk of experiencing a blood clot compared to someone with breast cancer. Cancer patients who are overweight are more at risk than those with a normal BMI, and patients who need to be treated with surgery are at greater risk than patients being treated with chemotherapy alone.

As a result, it is crucial to evaluate a patient’s numerous risk factors before deciding whether their cancer alone poses the greatest threat to their lifespan and quality of life, or if it is their risk of developing a fatal blood clot. In general, bleeding risk should also be considered as patients with cancer have an increased risk of bleeding with anticoagulation than patients without cancer on anticoagulation, although the risk of a recurrent blood clot is higher than the risk of bleeding. Since cancer is often treated with a multi-modality treatment method, an oncologist may decide to treat with chemotherapy to shrink a patient’s tumor before opting for surgery. Some types of treatments are also associated with an increased risk for developing a blood clot, including radiation and immunotherapy.

Identifying Personal Risk

As modern medicine continues to advance, so do our capabilities for evaluating someone’s risk factors prior to deciding on a treatment plan. Recent studies have selected cancer patients based on their Khorana Risk Assessment Score to further evaluate whether the score is an accurate predictor of their risk for developing a blood clot. For someone recently diagnosed with cancer, this risk prediction score uses a number of factors to determine if they have a low, intermediate or high risk of developing a blood clot. Essentially, it identifies a patient’s baseline risk for developing thrombosis based on cancer type, some lab test results, and BMI. Since a patient’s risk of developing thrombosis is one of the most overlooked related conditions, this assessment score gives medical practitioners a way to determine if patients would benefit from starting low dose anticoagulation to prevent blood clots before they develop.

The Khorana Risk Assessment Score is a key validated tool in the thrombosis and cancer space. Within the last decade, there has been a huge shift in cancer prognoses because of treatment advancements. Direct oral anticoagulants (DOAC) are proven to prevent the formation of blood clots and to treat blood clots in cancer patients. According to studies published in the last five years, DOACs are an effective treatment for thrombosis in cancer patients, compared to the previously used low molecular weight heparin, decreasing the burden of the need for daily injections.  

As we look ahead, it is likely that the medical community will begin to actively identify high risk patients to prevent clots from ever forming, using DOACs at a lower dose than the treatment dose. It is crucial to try and prevent thrombosis in these patients, as blood clots may delay chemotherapy or cause other complications which can affect their cancer treatments.

The Path Forward

In addition to medical advances and appropriate medical assessment, patient advocacy and awareness is also a key element of prevention and early identification. The World Thrombosis Day campaign, dedicated annually on October 13, spreads life-saving awareness about the signs, symptoms, and risk factors for thrombosis. Patients can advocate for their own well-being by informing medical professionals of their family health history, personal risk factors and normal physical state.

It is equally important for primary care doctors and oncologists to be cognizant of the heightened risk factors for cancer patients and be diligent in informing them of all these risks to ensure they understand their treatment and can take proper precautions and preventative measures.

Taking Preventative Action

Awareness has the power to prevent the occurrence of thrombosis in patients with cancer. As cancer treatments improve, patients are living longer with cancer. However, as cancer evolves into more of a chronic condition, patients are at risk of developing related conditions for a much longer period. If you or someone you know has cancer, resources like this, from World Thrombosis Day, can ensure you are prepared to combat the second leading cause of death for cancer patients.