A translational model-based approach to inform the choice of the dose in phase 1 oncology trials: the case study of erdafitinib.

PURPOSE: Erdafitinib (JNJ-42756493, BALVERSA) is a tyrosine kinase inhibitor indicated for the treatment of advanced urothelial carcinoma. In this work, a translational model-based approach to inform the choice of the doses in phase 1 trials is illustrated. METHODS: A pharmacokinetic (PK) model was developed to describe the time course of erdafitinib plasma concentrations in mice and rats. Data from multiple xenograft studies in mice and rats were analyzed using the Simeoni tumor growth inhibition (TGI) model. The model parameters were used to derive a range of erdafitinib exposures that might inform the choice of the doses in oncology phase 1 trials. Conversion of exposures to doses was based on preliminary PK assessments from the first-in human (FIH) study. RESULTS: A one-compartment PK disposition model, with linear absorption and dose-dependent clearance, adequately described the PK data in both mice and rats via an allometric scaling approach. The TGI model was able to describe tumor growth dynamics, providing quantitative measurements of erdafitinib antitumor potency in mice and rats. Based on these estimates, ranges of efficacious unbound concentration were identified for erdafitinib in mice (0.642-5.364 µg/L) and rats (0.782-2.565 µg/L). Based on the FIH data, it was possible to transpose exposures into doses and doses of above 4 mg/day provided erdafitinib exposures associated with significant TGI in animals. The findings were in agreement with the results of the FIH trial, in which the first hints of clinical activities were observed at 6 mg. CONCLUSION: The successful modeling exercise of erdafitinib preclinical data showed how translational PK-PD modeling might be a tool to help to inform the choice of the doses in FIH studies.

Modeling tumor growth inhibition and toxicity outcome after administration of anticancer agents in xenograft mice: A Dynamic Energy Budget (DEB) approach.

Host features, such as cell proliferation rates, caloric intake, metabolism and energetic conditions, significantly influence tumor growth; at the same time, tumor growth may have a dramatic impact on the host conditions. For example, in clinics, at certain stages of the tumor growth, cachexia (body weight reduction) may become so relevant to be considered as responsible for around 20% of cancer deaths. Unfortunately, anticancer therapies may also contribute to the development of cachexia due to reduced food intake (anorexia), commonly observed during the treatment periods. For this reason, cachexia is considered one of the major toxicity findings to be evaluated also in preclinical studies. However, although various pharmacokinetic-pharmacodynamic (PK-PD) tumor growth inhibition (TGI) models are currently available, the mathematical modeling of cachexia onset and TGI after an anticancer administration in preclinical experiments is still an open issue. To cope with this, a new PK-PD model, based on a set of tumor-host interaction rules taken from Dynamic Energy Budget (DEB) theory and a set of drug tumor inhibition equations taken from the well-known Simeoni TGI model, was developed. The model is able to describe the body weight reduction, splitting the cachexia directly induced by tumor and that caused by the drug treatment under study. It was tested in typical preclinical studies, essentially designed for efficacy evaluation and routinely performed as a part of the industrial drug development plans. For the first time, both the dynamics of tumor and host growth could be predicted in xenograft mice untreated or treated with different anticancer agents and following different schedules. The model code is freely available for downloading at http://repository.ddmore.eu (model number DDMODEL00000274).

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development.

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Resistance Development: A Major Piece in the Jigsaw Puzzle of Tumor Size Modeling.

Mathematical models of tumor size (TS) dynamics and tumor growth inhibition (TGI) need to place more emphasis on resistance development, given its relevant implications for clinical outcomes. A deeper understanding of the underlying processes, and effective data integration at different complexity levels, can foster the incorporation of new mechanistic aspects into modeling approaches, improving anticancer drug effect prediction. As such, we propose a general framework for developing future semi-mechanistic TS/TGI models of drug resistance.

Mathematical modeling of growth and death dynamics of mouse embryonic stem cells irradiated with γ-rays.

Following ionizing radiation, mouse embryonic stem cells (mESCs) undergo both apoptosis and block at G2/M phase of the cell cycle. The dynamics of cell growth and the transition through the apoptotic phases cannot be directly inferred from experimental data, limiting the understanding of the biological response to the treatment. Here, we propose a semi-mechanistic mathematical model, defined by five compartments, able to describe the time curves of untreated and γ-rays irradiated mESCs and to extract the information therein embedded. To this end, mESCs were irradiated with 2 or 5 Gy γ-rays, collected over a period of 48 h and, at each time point, analyzed for apoptosis by using the Annexin V assay. When compared to unirradiated mESCs, the model estimates an additional 0.2 probability to undergo apoptosis for the 5 Gy-treated cells, and only a 0.07 (not statistically significantly different from zero) when a 2 Gy-irradiation dose is administered. Moreover, the model allows us to estimate the duration of the overall apoptotic process and also the time length of its early, intermediate, and late apoptotic phase.

White paper: landscape on technical and conceptual requirements and competence framework in drug/disease modeling and simulation.

Pharmaceutical sciences experts and regulators acknowledge that pharmaceutical development as well as drug usage requires more than scientific advancements to cope with current attrition rates/therapeutic failures. Drug disease modeling and simulation (DDM&S) creates a paradigm to enable an integrated and higher-level understanding of drugs, (diseased)systems, and their interactions (systems pharmacology) through mathematical/statistical models (pharmacometrics)(1)-hence facilitating decision making during drug development and therapeutic usage of medicines. To identify gaps and challenges in DDM&S, an inventory of skills and competencies currently available in academia, industry, and clinical practice was obtained through survey. The survey outcomes revealed benefits, weaknesses, and hurdles for the implementation of DDM&S. In addition, the survey indicated that no consensus exists about the knowledge, skills, and attributes required to perform DDM&S activities effectively. Hence, a landscape of technical and conceptual requirements for DDM&S was identified and serves as a basis for developing a framework of competencies to guide future education and training in DDM&S.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e40; doi:10.1038/psp.2013.16; advance online publication 1 May 2013.

A minimal model of tumor growth inhibition in combination regimens under the hypothesis of no interaction between drugs.

One important issue in the preclinical development of an anticancer drug is the assessment of the compound under investigation when administered in combination with other drugs. Several experiments are routinely conducted in xenograft mice to evaluate if drugs interact or not. Experimental data are generally qualitatively analyzed on empirical basis. The ability of deriving from single drug experiments a reference response to the joint administrations, assuming no interaction, and comparing it to real responses would be key to recognize synergic and antagonist compounds. Therefore, in this paper, the minimal model of tumor growth inhibition (TGI), previously developed for a single drug, is reformulated to account for the effects of noninteracting drugs and simulate, under this hypothesis, combination regimens. The model is derived from a minimal set of basic assumptions that include and extend those formulated at cellular level for the single drug administration. The tumor growth dynamics is well approximated by the deterministic evolution of its expected value that is obtained through the solution of an ordinary and several partial differential equations. Under suitable assumptions on the cell death process, the model reduces to a lumped parameter model that represents the extension of the very popular Simeoni TGI model to the combined administration of noninteracting drugs.