Is life sciences ready for post-pandemic transformation?
Preparing for the next era of clinical development and therapy commercialization, companies must choose: move forward with innovations spurred by the pandemic or retrench into default practices.
The pandemic sparked the race to develop a COVID-19 vaccine. It also catalyzed new approaches to clinical trials and commercialization. Whether companies started to implement decentralized trials technology, explore external control arm studies or discover new sources and applications of real-world data (RWD), I hope the march toward innovative improvements to get therapies – and vaccines for evolving diseases – to patients safely and quickly will continue. There is no turning back to the way things used to be. The future is here, and we need to embrace it.
Moving forward with digital transformation
Our industry has traditionally been slower on the technology adoption curve with good reason. We are first and foremost guardians of patient safety, ensuring quality standards for care. Our regulatory and legal requirements exist to protect patients and promote public health and well-being.
At the same time, most life sciences professionals are passionate about getting safe and effective treatments to patients faster. The pandemic gave our industry the impetus to advance tech-enabled workflows, such as:
- Investigators extracting data from patient wearables for clinical trials
- Providers connecting remotely to patients and investigators
- Sponsors aggregating clinical data from multiple sources and translating it into regulatory grade submissible files to the authorities
The technologies that enable these processes, workflows and solutions have been around for a while, but companies had been slow to adopt them. I believe the pandemic – coupled with the energy and opportunistic perspective of life sciences teams – accelerated the adoption of tech-enabled solutions by five to 10 years.
Better approaches with RWD, remote monitoring
The pandemic also changed the way companies execute steps in a therapeutic’s lifecycle. For example, there is more industry interest in, and regulatory acceptance of, RWD especially in two use cases:
1. Patient-generated RWD that life sciences companies can standardize to create regulatory-grade data for clinical trials to derive insights and demonstrate outcomes. The challenge here is to make sure it is high enough quality to incorporate into the clinical trial dossier and meet requirements of regulatory agencies, such as the U.S. Food and Drug Administration (FDA).
2. Patient data that is not specifically for a clinical trial, but rather clinical data that creates a longitudinal health record. Life sciences can use this data as external control arms, which can be a more effective alternative to randomized control trials in certain situations like rare diseases.
In addition to these changes in approach, I think we will also continue to see remote patient and site monitoring via telehealth in the clinical trial setting. For example, telehealth can avoid the need for patients to come into the clinic to provide biometrics and related data and pharmaceutical companies can monitor clinical trial sites’ reporting and date collection processes. Telehealth can supplement and even replace these activities.
I’ve been intentional about staying close with our clients as they adopted remote and tech-enabled workflows. For those who don’t want to revert to the old ways of doing things, they are looking for a playbook about decentralized trials and the appropriate workflows, models and standards.
Preparing for the “next normal” in life sciences
As pharmaceutical and CRO companies come back online, the industry is seeing the rate of new studies increase. I expect this pent-up demand to create an explosion in the number of trials and desire to get those trials out there faster. I think we’ll see life sciences companies eager to embrace tech-enabled improvements.
I expect the industry will also continue to see growth in research for rare diseases, which requires significant effort to ready the data for studies. Life sciences companies must find identify hard to reach rare disease patients and uncover as much as they can about genotype, phenotype, clinical, claims and wearable data. They’ll need to be able to ingest, link and enrich this disparate data to create more complete patient records. This is not something they can do alone – hence the need for trusted partners to bridge the data-to-study gap to solve for research challenges.
I also expect the industry will see remarkable advances with the application of artificial intelligence (AI), including natural language processing (NLP), advanced analytics, voice recognition and more. For example, IBM has started partnering with Boston Scientific to use AI and advanced analytics for pain quality management.
Advanced AI approaches are also revolutionizing disease progression modeling, especially developing precision therapeutics for rare diseases. Modeling disease progression – for conditions such as type 1 diabetes, Parkinson’s Disease or Huntington’s Disease – enables life sciences to get to subtypes of the disease on a genotypic level. Studying a subtype can enable them to better understand how to target therapies to the patients who will respond best.
The future holds so much promise and possibility. The industry is at a turning point: will it keep moving forward or retrench into pre-pandemic processes? Many of the clients I speak with recognize the value of moving forward with the technologies that will help get treatments to patients faster. Now is the time to prepare for the next era of clinical development and treatment commercialization.