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The Role of Quantitative Systems Pharmacology in First-in-Human Trial Design

Quantitative Systems Pharmacology (QSP) is a relatively new discipline with enormous potential to improve pharma R&D productivity and inform decision-making across the drug development process from early discovery to Phase 3. QSP combines computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process. QSP has already shown promise for increasing the probability of success in R&D by bridging scientific gaps between disciplines to enable target validation and is recognized by sponsors and global regulatory agencies as a valuable scientific approach to increase understanding of disease biology, improve target selection, and help to ensure drug safety and efficacy in clinical trials.

Why Use QSP for First-in-Human Trials?

QSP can also be used in the efficient design of First-in-Human (FIH) clinical trials to help determine the starting dose and subsequent dose escalations to ensure the best possible protection for human subjects. If FIH doses are estimated only on the basis of preclinical data, without including mechanistic model-based approaches such as QSP, investigators are not making the best use of all available data.

A Review of the Endocannabinoid System for Treating Pain: A Prelude for Endorsing the Use of QSP in FIH Studies

The endocannabinoid system (EC) is involved in many physiological processes in the central and peripheral nervous systems such as pain-sensation, appetite, mood, and memory. Modulating EC system activity has been investigated by the pharmaceutical industry for its potential to treat a wide range of diseases including neuropathic pain, cardiovascular diseases, Parkinson’s and Huntington’s disease, and many others. The identification of cannabinoid receptors, CB1 and CB2, belonging to the superfamily of G protein-coupled receptors (GPCRs), and their endogenous lipid ligands, spurred research into therapeutic compounds that inhibit EC metabolism and transport, e.g. fatty acid amide hydrolase (FAAH), a membrane-bound serine hydrolase which degrades endocannabinoids in the brain.

Historically, FIH studies and early stage clinical trials have been conducted with a notable safety record. However, the 2016 tragic outcome of the FIH trial on BIA 10-2474, a FAAH inhibitor, which led to the death of one volunteer and produced mild-to-severe neurological symptoms to four others, resulted in the European Medicines Agency (EMA) revising their guideline on pre-clinical and clinical aspects of FIH and early clinical trials. Although the clinical neurotoxicity is still unclear, activity-based protein profiling studies to determine the protein interaction landscape of the test compound in human cells and tissues has shown that the high doses of BIA 10-2474 administered may have attributed to off-target activities of BIA 10-2474 leading to severe adverse effects.

Incorporating a QSP Approach to FIH to Avoid Severe Adverse Outcomes in FIH Trials

Last year in a Clinical Pharmacology and Therapeutics (CPT) Letter to the Editor, we proposed that if a QSP modeling approach – that complements conventional pre-clinical standards in translational drug development – was used in the BIA 10-2474 FIH trial, the disastrous outcomes of the trial could have been avoided. QSP modeling would have provided a more meaningful prediction of the pharmacodynamic range and maximum dose for the BIA 10-2474 FIH than pre-clinical animal data.

The CPT letter illuminates how while the EMA and the pharmaceutical industry agree on how the new guideline emphasizes the better use of pre-clinical data to guide rational dose selection of FIH studies, they differ in their perspective on defining the pharmacodynamic range and maximum dose that can be explored in a FIH study. The industry and regulatory view suggests that FIH doses can only be estimated on the basis of pre-clinical data. This stance ignores the promising role of using QSP, and other mechanistic modeling approaches, which may or may not use pre-clinical data.

To validate the value of incorporating a QSP approach for the BIA 10-2474 FIH trial, we highlighted the results of a QSP model we published in 2014 that identified gaps in the field’s understanding of the pathway. Our model helped explain why the selective FAAH inhibitor PF-04457845 failed in Phase II testing by Pfizer for osteoarthritic pain. In the absence of relevant pre-clinical animal models of pain, the QSP model was entirely based on and calibrated against in vitro and human literature data. In the PF-04457845 study, the QSP model predicted a limited modulation in the brain of the target of interest CB1 – the magnitude of which would saturate at relatively low doses of the test compound. Based on similarity of the biomarker anandamide data from both the PF-04457845 and BIA 10-2474 studies, the QSP model’s conclusions in the 2014 FAAH inhibitor study could have forewarned that the high daily dose of BIA 10-2474 was beyond what was needed to saturate the target pharmacology.

The Future of QSP in FIH Studies

We believe that mechanistic models complement conventional pre-clinical standards in translational drug research and should be more widely adopted by drug developers, encouraged and supported by regulators, and included in future guidelines. In the response to our CPT Letter to the Editor, regulators in the EMA “welcomed the initiative shown” and stated, “Mechanistic models leading to further refinement of the predictions from standard pre-clinical procedures and the use of additional drug-specific or mechanistic data or considerations are encouraged. Relevant models holding the potential to better reflect a substance’s effects in human tissues and potentially improve the safety of trial participants will be supported by the EMA.”

At Certara, we routinely employ QSP models – modular in form and extendable whenever new biological insights become available – to support clinical trial designs for a variety of mechanisms and indications. Our two QSP consortia— on immunogenicity and immune-oncology— represent members from leading biopharmaceutical companies who will help to continue and advance the development of QSP models for drug development.


  1. van der Graaf, P. & Benson, N. The role of quantitative systems pharmacology (QSP) in the design of first-in-human trials (2018). Pharmacol. Ther. 104(5), 797.
  2. Blake, K., Bonelli, M., Ponzano, S., Enzmann, H. (2018). Response to: “The Role of Quantitative Systems Pharmacology in the Design of First‐in‐Human Trials” Pharmacol. Ther. 104(5), 798.
  3. EMA Guideline on strategies to identify and mitigate risks for first-in-human and early clinical trials with investigational medicinal products
  4. Van Esbroeck, A., Janssen, A., Cognetta, A., et al. (2017). Activity-based protein profiling reveals off-target proteins of the FAAH inhibitor BIA 10-2474. Science 356, 1084-1087.
  5. Ponzano, S., Blake, K, Bonelli, M., Enzmann, H. (2018). Promoting safe early clinical research of novel drug candidates: a European Union regulatory perspective. 103(4), 564-566.
  6. DeGeorge, J., Robertson, S., Butler, L., et al. (2018). An industry perspective on the 2017 EMA guideline on first-in-human and early clinical trials. 103(4), 566-568.
  7. Benson, N. et al. (2014) A systems pharmacology perspective on the clinical development of fatty acid amide hydrolase inhibitors for pain. CPT Pharmacometrics Syst. Pharmacol. 3, e91
  8. Tuzman, KT (2018). Models first. How Quantitative Systems Pharmacology can pick the right dose for First-in-Human trials Biocentury Innovations. August 2, 2018.

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By: Piet van der Graaf

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