2018 - Montreux - Switzerland

PAGE 2018: Drug/Disease modelling - CNS
Tomomi Matsuura

Clinical validation of a quantitative systems pharmacology (QSP) model for nerve-growth-factor (NGF) therapies

Tomomi Matsuura, Mike Walker, Divyanshi Karmani, Neil Benson and Piet H. van der Graaf

Certara, QSP, Canterbury CT2 7FG, United Kingdom

Objectives: Despite a marked increase in the application of quantitative systems pharmacology (QSP), its role in drug development and regulatory decision making has not yet been well established. Case studies presented to date have often been retrospective in nature and may not be considered compelling enough by decision makers and regulators to significantly increase their confidence in the approach. On the other hand, due to fact that QSP is often applied in early stages of research [1], prospective predictions may never reach the point of clinical validation due to high attrition rates. Therefore, there is an urgent need for sharing case studies of clinical validation of prospective QSP model predictions [2].

Methods: Previously, we developed a QSP model of the nerve growth factor (NGF) pathway to guide selection and validation of novel targets for the treatment of pain [3]. A key, non-intuitive prediction from the model was that the concentrations to achieve clinically-meaningful analgesic efficacy for a small-molecule inhibitor of the tropomyosin receptor kinase A (TrkA) are very high (i.e. ~100-fold the in vitro potency). Clinical results in a battery of human evoked pain models for the first “peripherally-restricted pan-Trk inhibitor”, PF-06273340, were very recently published [4] and we analysed the pharmacokinetic-pharmacodynamic (PKPD) results and compared this with our previous QSP predictions. PKPD data were extracted from the publication [4] with WebPlotDigitizer and analysed using the semi-compartmental modelling approach proposed by Kowalski and Karim [5], implemented in Phoenix version 7.

Results: PF-06273340 only met the decision rule on one of the five primary endpoints (UVB skin thermal pain detection threshold) at the highest dose tested (400 mg). Graphical exploration of the PKPD relationship showed marked hysteresis. An apparently linear effect-site concentration-effect relationship was obtained using a semi-compartmental model [5]. Through extrapolation of the model, we estimated the efficacious concentration required to mirror the effect observed with reference treatment (600 mg ibuprofen) to be 795ng/ml, which is ~275-fold and ~20-fold the primary pharmacology IC50 based on total and unbound plasma concentration, respectively. Assuming that these are estimates of the lower end of the concentration-effect relationship and that higher multiples are required to match the clinical efficacy of NGF monoclonal antibodies, we conclude that the data are consistent with the prospective QSP model predictions for a selective TrkA inhibitor [3], also because some of efficacy observed with PF-06273340 may be related to its TrkB activity [4]. This potential gain in efficacy would however be offset by a smaller therapeutic window over TrkB-mediated side effects in the central nervous system [6]

Conclusions: This analysis provides clinical validation of our QSP model for NGF therapies in pain. Given the very high predicted multiples of IC50 required to produce efficacy better than standard of care, we propose that it may not be possible to develop a peripherally-restricted small-molecule TrkA inhibitor with an acceptable therapeutic index for the treatment of pain. The quantitative guidance provided by the QSP model for the first-in-human study design is in line with new regulatory expectations with regards to calculation of starting dose and subsequent dose escalations [7]. Due to the modular nature of the model, it can be updated and extended when new biological insights become available [8] and to date we have applied it to 5 programs of different compounds/mechanisms. A mathematically reduced version of the full model has also been developed, which is more amenable for pharmacometric applications in clinical development [9].

[1] Nijsen MJMA et al. Preclinical QSP Modeling in the Pharmaceutical Industry: An IQ Consortium Survey Examining the Current Landscape. CPT Pharmacometrics Systems Pharmacol. (2018), DOI:10.1002/psp4.12282
[2] Hendriks B. Negative modelling results: a dime a dozen or a stepping stone to scientific discovery? CPT: Pharmacometrics & Systems Pharmacol. (2013) 2, e48, DOI:10.1038/psp.2013.28
[3] Benson N, Matsuura T, Smirnov S, Demin O, Jones HM, Dua P & van der Graaf PH. Systems pharmacology of the nerve growth factor pathway: use of a systems biology model for the identification of key drug targets using sensitivity analysis and the integration of physiology and pharmacology. Interface Focus (2013) 3, DOI 10.1098/rsfs.2012.0071
[4] Loudon P, Siebenga P, Gorman D , Gore K, Dua P, van Amerongen G, Hay JL, Groeneveld GJ & Butt RP. Demonstration of an anti-hyperalgesic effect of a novel pan-Trk inhibitor PF-06273340 in a battery of human evoked pain models. Br. J. Clin. Pharmacol. (2017), DOI:10.1111/bcp.13448
[5] Kowalski KG & Karim A. A semicompartmental modeling approach for pharmacodynamics data assessment. J. Pharmacokinet. Biopharm. (1995), 23(3):307-322.
[6] Skerratt SE et al. The Discovery of a Potent, Selective, and Peripherally Restricted Pan-Trk Inhibitor (PF-06273340) for the Treatment of Pain. J. Med. Chem. (2016), 59: 10084-10099, DOI: 10.1021/acs.jmedchem.6b00850.
[7] Guideline on strategies to identify and mitigate risks for first-in -human and early clinical trials with investigational medicinal products. European Medicines Agency, 20 July 2017.http://www.ema.europa.eu/ema/index.jsp?curl=pages/news_and_events/news/2017/07/news_detail_002783.jsp&mid=WC0b01ac058004d5c1
[8] Toni T, Dua P & van der Graaf PH. Systems Pharmacology of the NGF Signaling Through p75 and TrkA Receptors. CPT Pharmacometrics & Systems Pharmacol. (2014) 3, e150; DOI:10.1038/psp.2014.48
[9] Snowden TJ, van der Graaf PH & Tindall MJ. A combined model reduction algorithm for controlled biochemical systems. BMC Systems Biology (2017) 11:17, DOI 10.1186/s12918-017-0397-1

Reference: PAGE 27 (2018) Abstr 8465 [www.page-meeting.org/?abstract=8465]
Poster: Drug/Disease modelling - CNS

Link to DDMoRe model repository:https://www.ebi.ac.uk/biomodels-main/BIOMD0000000588
Click to open PDF poster/presentation (click to open)