I recently attended the Sanofi – MSISB Mount Sinai Systems Pharmacology Symposium. This one day meeting focused on the ways quantitative systems pharmacology approaches can be used throughout the drug development process and how these approaches can gain larger prominence within the industry. Speakers from academia, industry, and the FDA presented their success stories and vision for the future. In this short post, I will share what I took away from the meeting.
With greater availability of data, advances in computing, and the previous successes of quantitative approaches, mechanistic modelling is gaining prominence under the label “quantitative systems pharmacology” (QSP). Many of the speakers at the symposium discussed the benefits of QSP. A QSP approach quantifies the mental model and assumptions that the research team is working from, and provides a framework for integrating data and making predictions. A QSP model also provides insight when it fails to fit the data; highlighting gaps in knowledge that can in turn suggest new experiments and prioritize future studies.
The standard modeling approach that is used within the pharmaceutical industry is a data-driven, empirical approach. This brute-force approach requires many experimental tests and statistical analysis to obtain a set of equations that can reproduce the observed behavior. These models are relatively inexpensive to produce, but are problem specific and only valid within the range of experimentally observed data. On the other hand, QSP models take much longer to develop but the extra cost can be worthwhile since QSP models are not restricted to a “range of validity” and so can be used to extrapolate from observed values to make predictions. These predictions can be used to translate observations from one system to another, allowing researchers to make predictions in human populations based on animal models for example.
Another advantage of QSP models is that they are not “single-use” products, and can be used during the drug development process for many compounds that affect the system covered by the model. During some of the informal discussions it came out that this aspect of QSP was the foundation of the business plan for companies like Rosa & Co. and Applied BioMath, who develop large models based on basic biology (described as “pre-competitive” models) and then incorporate the client company’s proprietary data to create a system-specific model for the compound under investigation. I have also come across the company DILIsym, that has created a mechanistic model of drug induced liver injury that they license to companies to support risk-assessment and decision making related to new compounds.
Perhaps the biggest take-away for me was a renewed appreciation for the power of mathematical modeling that I discovered as a student. The speakers presented concrete examples of how mathematical modeling contributed to advances in care for people with heart arrhythmia, kidney disease, TB, and other conditions. These examples demonstrate the powerful role mathematics can play, and are a preview of the changes to come within the pharmaceutical industry as quantitative systems pharmacology approaches gain more traction.