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By Riaz Bandali and George Scott

Judging only from the number of new drug approvals in the U.S., biopharma’s global engine of innovation would seem to be firing on all cylinders. Last year, the U.S. Food and Drug Administration approved 46 new molecular entities (NMEs), the highest number in two decades, and more than twice the tally in 2016.[1] Ten months into 2018, the FDA had already beat that number, and the agency aims to further accelerate the process through innovations in clinical trial design, wider use of surrogate endpoints and other mechanisms.[2]

Patterns in NME approvals are one gauge of industry health and may be a valid cause for optimism. By other metrics, however, the infrastructure we rely on to create new medicines is in sore need of an upgrade. More and more investigational drugs are streaming into the pipeline. But most of them fail in clinical trials, draining corporate resources, disappointing investors, and shattering the hopes of patients. Estimating the return on investment for 12 large cap biopharma companies, the Deloitte Center for Health Solutions found that R&D returns fell from 10.1 percent in 2010 to just 3.2 percent last year,[3] and could hit 0 in 2020 — meaning each research dollar returns one dollar.[4]

In most industries, increasing the number of “shots on goal” is a worthy strategy. In biopharma, it can lead down a troubled path because most of the “shots” are poorly validated or come with unsustainable costs. Translational science — the evidence-based process of converting research findings into tangible health improvements — may help solve this puzzle by providing the tools needed to assess drug candidates upstream in development and prioritize early development candidates more effectively.

As the typical, small biopharma company plots its course to first-in-human trials, its efforts are often hindered by gaps in knowledge of the target disease area. Many SMIDs lack the infrastructure for selecting biomarkers and developing pharmacodynamic models, or for using pharmacology to assess mechanisms of action and efficacy and refine the functional model. Adding in pharmacokinetic insights, along with modeling and simulation techniques, can help companies predict in the preclinical stage how a drug will behave in the human body.

These are the targeted techniques and tools of translational science. Lacking the toolkit, biotech startups typically hire preclinical consultants who contract each step of the process out to different experts in the field. While that can be a good line of approach, we believe there’s an opportunity to transform this model.

Read more about the Translational Revolution, its value for small to mid-biopharmas and how adding translational science to your continuum of services is a critical step in raising success rates and converting more smart ideas into medicine.

[4] Further analysis of Deloitte numbers and underlying issues in Forbes at