There are numerous challenges in developing oncology drugs: (1) they are often very toxic which precludes conducting clinical trials in healthy volunteers, (2) the PK of a drug may be altered in cancer patients due to demographic and physiological differences as compared to healthy volunteers, and (3) cancer patients face elevated drug-drug interaction (DDI) risk due to concurrent treatment with multiple drugs to treat co-morbidities and treatment-associated side effects. A recent article in The Journal of Clinical Pharmacology1 reviews how modeling and simulation (M&S) approaches can impact dose selection and provide a risk-benefit assessment when developing an oncology drug.
Oncology drug developers are moving away from the historical toxicity-driven dose paradigm to a paradigm driven by target activity. The authors reviewed various clinical pharmacology tools and analyses including PK sampling, PK/PD relationships, exposure-response analyses, body weight-based dosing, and food effect that can help ensure the right dose is administered at the right time to the right patient. Targeted therapies, including immunotherapies, are being evaluated as cancer treatments and have promise to become the new standard in treatment.
PK sampling following single- and multiple-dose administration in early development—first-in-man (FIM) studies and late-stage Phase 2-3 studies—are critical in characterizing the PK of the drug and will help in the evaluation of exposure-response relationships across dose levels.
One criteria to consider in PK sample collection during FIM studies is the difference between small and large molecule half-lives. The former require hourly measurements resulting in sample collection over the entire dosing interval. Conversely, large molecule therapeutics have long half-lives, measured over days versus hours.
Characterizing the area under the time-concentration curve, elimination half-life, peak concentration, and other rudimentary PK parameters will provide more informed decision making during the dose escalation process and a better understanding of the exposure-response profile of the drug. Using PopPK modeling for PK samples collected in late-stage Phase 2-3 studies, which have broader patient populations, will allow investigating the effects of intrinsic and extrinsic variables of the drug’s PK and link to safety and efficacy biomarkers.
In addition to PK sampling, PK/PD relationships and molecular markers of response can be explored by measuring PD markers in clinical trials. Although molecular markers of response are rarely correlated to clinical response, understanding PK/PD relationships can help to support dose justifications, validate mechanisms of action, and provide proof of concept for an investigative drug. An example of where evaluating PD markers were used for dose justification is highlighted in a study on BCR-ABL small molecule kinase inhibitors that were approved for treatment of chronic myeloid leukemia. The study data indicated that the PK/PD is correlated with molecular response at 3 months, which suggests that assessment of PD markers,2 included in larger clinical trials, can bolster dose justification arguments. This is particularly useful in cases where the dose- or exposure-response relationships can be distinguished.
Exposure-response analysis—which depends on PK sampling incorporated throughout development—can be a valuable tool to provide additional assurance of drug safety and efficacy. Understanding the effect of concentration on response and toxicity can aid drug developers and regulators to develop dose adjustments in cases of altered PK.
In one example, both physiologically-based pharmacokinetics (PBPK) and PK/PD M&S were conducted to predict exposure alterations in Ibrutinib, a cytochrome P450 3A4 substrate, due to co-administration of ketoconazole, a strong CYP3A4 inhibitor or rifampin, a strong CYP3A4 inducer. The results, based on the use of PK/PD modeling and known exposure-response relationships of Ibrutinib in the presence of moderate and weak CYP3A4 inhibitors and inducers, were used to determine dose modifications for the concomitant use of Ibrutinib with CYP3A4 modulators. The combination of well-characterized exposure-response with robust dose ranging and M&S can support optimized precision dosing for labeling.
Body weight-based dosing
The decision to use either flat-fixed or body weight-based dosing depends on the effect of body weight on a drug’s clearance, the volume of distribution, the drug’s PD, and its therapeutic margin for safety and efficacy. An effective dosing strategy reduces interpatient PK variability and should maximize therapeutic outcomes. Currently, flat-fixed dosing is preferred for biologics due to its ease in administration, decreased likelihood in dosing errors, reduced waste, and lower cost of goods. Flat-fixed dosing is recommended for FIM studies of protein and peptide therapeutics as it results in less inter- and intra-subject variability in exposure compared to weight-based dosing. Overall, the dosing strategy for Phase 3 trials should be determined based on assessing body size effect on PK/PD after the drug’s therapeutic window is established.
For orally-administered drugs, whether the patient is fed or fasted can affect bioavailability and hence safety and efficacy. Having an early understanding of the effect of food combined with the ability to administer a drug with and without food could reduce gastrointestinal toxicity and variability and increase patient compliance.
M&S is now a regulatory necessity
The US FDA Prescription Drug User Fee Act (PDUFA) for fiscal years 2018–2022 reflects the agency’s goals to expedite bringing safer therapies to patients. It also reflects and incorporates the advances in regulatory science and decision-support tools, such as M&S, to support drug development and decision-making. M&S tools have demonstrated their importance in dose selection, especially in special populations, dose optimization and dosing regimen, characterizing exposure-response, predicting drug-drug interactions, and more. As M&S tools are increasingly used in clinical studies, its importance will be reinforced in advancing drug development.
 Bullock JM, Lin T, & Bilic S. (2017). Clinical pharmacology tools and evaluations to facilitate comprehensive dose finding in oncology: A continuous risk-benefit approach. J. Clin. Pharmacol., 57(510), 5105–5115.
 Ishida Y, Murai K, Yamaguchi K, et.al. (2016). Pharmacodynamics and pharmacodynamics of dasatinib in the chronic phase of newly diagnosed chronic myeloid leukemia. Eur. J. Clin. Pharmacol., 72(2), 185–193.
 US Department of Health and Human Services, Food and Drug Administration, Center
for Drug Evaluation and Research. (2016). Clinical Pharmacology Review, Application Number 205552Orig1s000. Retrieved from https://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/205552Orig1s000ClinPharmR.pdf
To learn more on how drug exposure in individual patients can be better predicted using Virtual Twin Technology, please watch this webinar by my colleague, Dr. Tom Polasek.