Using Simcyp-guided ADME Biomarker Discovery to Prospectively Identify Patients at High Risk of Drug Toxicity

Using Simcyp-guided ADME Biomarker Discovery to Prospectively Identify Patients at High Risk of Drug Toxicity

As novel molecular targets are being continuously discovered and new treatments developed, oncology is one of the biggest therapeutic areas in precision medicine. In particular, new targeted anti-cancer medications that are taken orally, such as the protein kinase inhibitors (KIs), are ideal candidates for model-informed precision dosing (MIPD) technologies.1 One of these technologies is called physiologically-based pharmacokinetic (PBPK) modeling and simulation (M&S). PBPK M&S has been used extensively to evaluate the pharmacokinetics (PK) of oncology drugs for dose selection in clinical trials and to predict the potential clinical relevance of PK drug-drug interactions.2,3 A significant number of cancer patients taking KIs experience treatment-limiting toxicity, but there is currently no way of prospectively identifying them so that doses can be adjusted. Recently, Certara’s PBPK platform, the Simcyp® Simulator, has been investigated as an approach that could predict which patients are more likely to experience drug toxicity.

The protein KIs are a chemically diverse group of drugs used in oncology and hematology. Between patient variability in the PK of KIs is dependent largely on the activities of CYP3A4/5 and P-gp. Indeed, KIs are very sensitive “victims” of PK-DDIs. KIs are traditionally dosed using toxicity guided dosing—the dose is increased until the maximum dose is reached and then scaled back only when adverse effects become intolerable. This approach will insure adequate drug exposure to treat the cancer but is unpleasant for patients. There is growing evidence that PK-guided dosing of KIs to aid achieving steady state concentrations within the therapeutic window (ie, therapeutic drug monitoring) can maintain treatment efficacy and limit toxicities.4 This evidence means that KIs are also great candidates for MIPD.

Dabrafenib is a good example. Dabrafenib is used to treat metastatic melanoma with mutated isoforms of the BRAF gene, V600E and V600K.5 Dabrafenib is an inhibitor of the BRAF gene product, B-Raf, which plays an essential role in cell growth regulation. However, dabrafenib resistance typically occurs after about 6 months of monotherapy and cancer progresses. To address this, the FDA recently approved the combination therapy of dabrafenib together with another KI, trametinib, which inhibits mitogen-activated extracellular kinases, MEK1 and 2. Although the combination has survival benefits, about 1/3 of patients experience adverse effects leading to dose reduction and sometimes treatment cessation. A recent study demonstrated that dabrafenib plasma concentrations above 48 ng/ml were associated with higher rates of toxicity.6

Simcyp-guided ADME biomarker discovery

Recently, investigators at Flinders University in Australia (Dr. Andrew Rowland) explored the idea of using Simcyp to identify the covariates that explain variability in PK.7 This is called Simcyp-guided ADME biomarker discovery. A full PBPK profile was built for dabrafenib. The FDA guidance was used to perform best practice PBPK M&S.8 The model was trained against single drug dose studies performed in male healthy volunteers. A univariant logistic regression analysis was used to screen for associations between the physiological and molecular characteristics of in silico individuals in the Genentech cancer population and dabrafenib concentration. Multi-variable analysis showed that consideration of baseline weight, body mass index, and CYP2C8, CYP3A4 and P-gp abundance could predict steady state dabrafenib trough concentrations above 48 ng/ml (ROC AUC 0.94, accuracy 88%).

The next step is to apply the Simcyp model of dabrafenib and the Virtual Twin™ approach to predict which patients are at increased risk of getting toxicities—the exciting part is that this can be done before they commence treatment.

Simcyp-guided ADME biomarker discovery represents a rapid, easy and cost-effective way to identify the major covariates driving between patient variability in PK. Once values for these parameters are known for an individual patient such as their CYP and transporter abundances, MIPD can predict PK for that patient. For drugs with a narrow therapeutic index, such as the KIs used to treat cancer, patients at higher risk of toxicity can be identified and their dose lowered to keep drug exposure within the therapeutic window. This maximizes the benefits of drug treatment for each patient whilst avoiding unnecessary harm from drug-induced toxicities.


References

[1] Rowland A, van Dyk M, Mangoni A, et al. (2017). Kinase inhibitor pharmacokinetics: Comprehensive summary and roadmap for addressing inter-individual variability in exposure. Opinion Drug Metab Toxicol, 13(1), 31–39.

[2] Saechang T, Na-Bangchang K, & Karbwang J. (2018). Utility of physiologically based pharmacokinetics modeling (PBPK) in oncology drug development and its accuracy: A systematic review. J Clin Pharmacol, 74, 1365–1376.

[3] Yoshida K, Budha N, & Jin JY. (2017). Impact of physiologically based pharmacokinetic models on regulatory reviews and product labels: Frequent utilization in the field of oncology. Cpt-journal.com, 101(5), 597–602.

[4] Lucas CJ & Martin JH. (2017). Pharmacokinetic-guided dosing of new oral cancer agents. Clin Pharmacol, 57(s10), s78–s98.

[5] Polasek TM, Ambler K, Scott HS, et al. (2017). Targeted pharmacotherapy after somatic cancer and mutation screening. F1000 Research, 5, 1551–1555.

[6] Rousset M, Dutriaux C, Bosco-Lévy P, Prey S, Pham-Ledard A, et al. (2017). Trough dabrafenib plasma concentration can predict occurrence of adverse events requiring dose reduction in metastatic melanoma. Clin ACTA, 472, 26–29.

[7] Rowland A, van Dyk M, Hopkins AM, et al. (2018). Physiologically based pharmacokinetic modeling to identify physiological and molecular characteristics driving variability in drug exposure. Pharmacol Therap, Epub ahead of print.

[8] US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research. (2018, August). Physiologically based pharmacokinetic analyses—format and content. Guidance for industry. Retrieved from https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM531207.pdf


To learn more about how model-based approaches can improve precision dosing in clinical care, watch this webinar.

Thomas Polasek

About the Author

Thomas Polasek

Clinical Pharmacologist, Consulting Services, Certara

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Dr. Tom Polasek is Clinical Pharmacologist at Certara and SA Health in South Australia. His research interests include all aspects of clinical pharmacology with a focus on the clinical application of M&S approaches. He is the author or co-author of more than 100 peer-reviewed articles and conference presentations. Tom earned his BPharm (Hons) from the University of South Australia and his BSc, MD and PhD degrees from Flinders University.