The global life sciences industry is witnessing a surge in CEO confidence from US tax reforms, turbulent equity markets, and the strengthening global economy.

The growth of the life sciences industry is boosted by healthcare digitization and democratization, which is creating an explosion in patient data, the emergence of value-based reimbursement models, and healthcare consumerism shifting the risk from payers to providers. The convergence of biopharmaceuticals, drug delivery devices, and companion diagnostics—enabled by digital connectivity is driving regulatory and commercial changes in many exciting ways for the industry. The Figure 1 below captures the key trends reshaping the future of the pharmaceutical industry.

Data Monetisation in Pharma R&D

2018 is envisioned to be the tipping point for pharmaceutical industry to leverage digitization and overcome several challenges in drug discovery and development. The most significant being around clinical trial recruitment; about 80 percent of pharma clinical trials do not meet enrolment deadlines, resulting in an average loss up to $1.3 million per day for a given drug candidate.

The increasing importance of patient centricity and value-based care is driving higher utilization of big data analytics in drug discovery and clinical trials management.

Within drug discovery, growth will be driven by increasing adoption of artificial intelligence and cloud-based digital platforms for medicinal chemistry, NGS informatics, pharmacovigilance, and drug safety solutions.

Within clinical development, enhancing clinical trial workflows has evolved as a key growth opportunity with a focus on data management, eClinical solutions (EDC, eSource, e.COA, RTSM solutions), patient recruitment, and monitoring IT solutions (mHealth, remote, and virtual clinical trials).

Patient Centricity Creating Aha Moment

Pharmacos have implemented patient-centric efforts (Figure 2) using digital tools to showcase benefits around empowering patients. This will allow patients to take control of their own health and share goals that are family centered.

Clinical trial enrichment techniques can deliver several benefits to both regulators and pharmacos.

Selecting patients with low variability across select baseline measurements (e.g., blood pressure, symptom score) and excluding patients whose disease or symptoms improve spontaneously can hugely decrease heterogeneity. Best practices include defining the right entry criteria such as patients having the disease being studied, likely compliance etc. A great example is Sanofi’s collaboration with Science37’s Metasite™ model and NORA technology to streamline the process of finding and retaining trial participants for the entire length of a study. Based on the estimates by Science 37, the minimum potential to reduce the time required for a typical trial is 30 percent.

Digital Continuity Can Revamp at mp Development

Cell and gene-based immuno-oncology therapies continue to be a key growth opportunity for bio-pharma companies. Although the largest number of marketed products is for dermatology and musculoskeletal diseases, the next wave is expected to be for oncology which accounts for approximately 50 percent of the pipeline.

Amidst this huge opportunity, the industry remains challenged on defining protocols around i licensing arrangements for gene transfer vectors and a consequent need to identify specific cell types) that will the drive choice of clinical trials. Targeting niche patient populations and ensuring high trial compliance rate further adds to this complexity. Existing information systems in the industry are disjointed and are not able to handle these business complexities. The net result is a big question on the legitimacy of safety and efficacy of the data.

The adoption of artificial intelligence and cloud-based digital solutions for drug discovery and clinical trial workflow is improving the overall productivity to develop innovative therapies

Prognostic enrichment techniques provide transparency and deliver full control to companies throughout the clinical development lifecycle. They help in selecting patients with a greater likelihood of having a disease-related endpoint event or a substantial worsening in the condition. For example, CNLP (Clinical Natural Language Processing) can be used to pre-screen patients, to potentially replace the cumbersome and expensive manual chart reviews.

Adapting to the New Normal

The paradox of the new growth rate is that it is taking place even as the life sciences industry is grappling with the larger question: When will precision healthcare become a reality? With US tax reforms now kicking in and the gistic effect of advanced technologies such as artificial intelligence, big data analytics, and cloud computing on the traditional life sciences industry, growth could be even faster in 2019-2020.

Innovators such as Medidata, IBM Watson, Oracle, Flatiron Health, BioClinica, Veeva Systems are poised to disrupt the drug discovery and development model. This is expected to accelerate industry collaborations to pursue best-of-breed technology and business model know-how.

Adoption of artificial intelligence and cloud-based digital solutions for drug discovery and clinical trial workflow is improving the overall productivity to develop innovative therapies.

Data monetization and e-commerce business models will open new revenue streams. Pharmacos will witness a paradigm shift from quantity-based, fee-for-service models to value- and outcomes-based contracts.