By combining disparate data into coherent mechanistic models, quantitative systems pharmacology is becoming a key tool for picking the right dose for first-in-human trials and other early make-or-break decisions. Advocates see it as part of an expanding toolbox of models that can yield better safety and efficacy predictions from preclinical data, and want regulators to include it in their guidances.

Moreover, they argue the disastrous outcome of the 2016 Phase I BIA 10-2474 trial could have been avoided if published QSP studies on parallel compounds had been taken into account.

QSP can enable companies to turn data into actionable decisions by translating biological mechanisms and lab measurements into mathematical equations and computational simulations. The models go beyond standard allometric scaling from finite animal data, to incorporate mechanistic information about what cellular compartments a molecule acts in, and how fast it is both produced and degraded, for example.

“The decisions in drug companies we have to make at a high level are -- ‘should we keep this program going forward’ and ‘what are the risks that we’re going to encounter with it?’” said Vikram Sinha, Associate VP of Quantitative Sciences at the Merck Research Laboratories unit of Merck & Co. Inc. “That’s where these models are becoming more and more influential, because this is the only way that we can integrate all this information.”


Karen Tkach Tuzman, Associate Editor, BioCentury (2018).

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