Translational modeling and simulation approaches for molecularly targeted small molecule anticancer agents from bench to bedside

Opportunities for Quantitative Translational Modeling in Oncology

A 2-day meeting was held by members of the UK Quantitative Systems Pharmacology Network () in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.

Pharmacokinetic-Pharmacodynamic-Efficacy Modeling of ONO-7579, a Novel Pan-Tropomyosin Receptor Kinase Inhibitor, in a Murine Xenograft Tumor Model

The orally available and novel small molecule ONO-7579 (N-{2-[4-(2-amino-5-chloropyridin-3-yl)phenoxy]pyrimidin-5-yl}-N'-[2-(methanesulfonyl)-5-(trifluoromethyl)phenyl]urea) is a highly potent and selective pan-tropomyosin receptor kinase (TRK) inhibitor. The objective of the present study was to characterize the pharmacokinetic (PK), pharmacodynamic (PD), and antitumor efficacy relationships of ONO-7579 in mice xenografted with a human colorectal cancer cell line, KM12 (harboring the tropomyosin 3 (TPM3) -neurotrophic tyrosine receptor kinase 1 fusion gene), via a PK/PD modeling approach. Plasma and tumor concentrations of ONO-7579, tumor levels of phosphorylated TPM3-TRKA (pTRKA), and tumor volumes in the murine model were measured with a single or multiple dose of ONO-7579 (0.06-0.60 mg/kg) administered once daily. The PK/PD/efficacy models were developed in a sequential manner. Changes in plasma concentrations of ONO-7579 were described with an oral one-compartment model. Tumor concentrations of ONO-7579 were higher than plasma concentrations, and changes in ONO-7579 tumor concentrations were described with an additional tumor compartment that had no influence on plasma concentrations. pTRKA in tumors was described with a direct E_max model, and the tumor ONO-7579 concentration causing 50% of the maximum effect was estimated to be 17.6 ng/g. In addition, a pTRKA-driven tumor growth inhibition model indicated that ONO-7579 started to sharply increase the antitumor effect at pTRKA inhibition rates >60% and required >91.5% to reduce tumors. In conclusion, the developed PK/PD/efficacy models revealed a "switch-like" relationship between pTRKA inhibition rate and antitumor effect in a murine KM12 xenograft model, demonstrating that pTRKA in tumors could serve as an effective biomarker for scheduling the dose regimen in early-stage clinical studies. SIGNIFICANCE STATEMENT: In recent years, clinical development of TRK inhibitors in patients with neurotrophic tyrosine receptor kinase fusion-positive solid tumors has been accelerated. This research found that phosphorylated TRKA was a useful biomarker for explaining the antitumor efficacy of TRK inhibitors using a pharmacokinetic/pharmacodynamic modeling approach in xenograft mice. This finding suggests a rational dosing regimen in early-stage clinical studies for ONO-7579 (N-{2-[4-(2-amino-5-chloropyridin-3-yl)phenoxy]pyrimidin-5-yl}-N'-[2-(methanesulfonyl)-5-(trifluoromethyl)phenyl]urea), a novel pan-TRK inhibitor.

A Microfluidic Perfusion Platform for In Vitro Analysis of Drug Pharmacokinetic-Pharmacodynamic (PK-PD) Relationships

Static in vitro cell culture studies cannot capture the dynamic concentration profiles of drugs, nutrients, and other factors that cells experience in physiological systems. This limits the confidence in the translational relevance of in vitro experiments and increases the reliance on empirical testing of exposure-response relationships and dose optimization in animal models during preclinical drug development, introducing additional challenges owing to species-specific differences in drug pharmacokinetics (PK) and pharmacodynamics (PD). Here, we describe the development of a microfluidic cell culture device that enables perfusion of cells under 2D or 3D culture conditions with temporally programmable concentration profiles. Proof-of-concept studies using doxorubicin and gemcitabine demonstrated the ability of the microfluidic PK-PD device to examine dose- and time-dependent effects of doxorubicin as well as schedule-dependent effects of doxorubicin and gemcitabine combination therapy on cell viability using both step-wise drug concentration profiles and species-specific (i.e., mouse, human) drug PK profiles. The results demonstrate the importance of including physiologically relevant dynamic drug exposure profiles during in vitro drug testing to more accurately mimic in vivo drug effects, thereby improving translatability across nonclinical studies and reducing the reliance on animal models during drug development.

Translational Pharmacokinetic-Pharmacodynamic Modeling for an Orally Available Novel Inhibitor of Epigenetic Regulator Enhancer of Zeste Homolog 2

PF06821497 has been identified as an orally available small-molecule enhancer of zeste homolog 2 inhibitor. The objectives of the present study were to characterize pharmacokinetic-pharmacodynamic-disease relationships of PF06821497 in xenograft mouse models with diffuse large B-cell lymphoma (Karpas422). An indirect-response model reasonably fit dose-dependent pharmacodynamic responses [histone H3 on lysine 27 (H3K27) me3 inhibition] with an unbound EC ₅₀ of 76 nM, whereas a signal-transduction model sufficiently fit dose-dependent disease responses (tumor growth inhibition) with an unbound tumor stasis concentration (T _sc ) of 168 nM. Thus, effective concentration for 70% of maximal effect (EC₇₀) for H3K27me3 inhibition was roughly comparable to T _sc , suggesting that 70% H3K27me3 inhibition could be required for tumor stasis. Consistently, an integrated pharmacokinetic-pharmacodynamic-disease model adequately describing tumor growth inhibition also suggested that ∼70% H3K27me3 inhibition was associated with tumor stasis. Based on these results, we would propose that an EC₇₀ estimate for H3K27me3 inhibition corresponding to tumor stasis could be considered a minimum target efficacious concentration of PF06821497 in cancer patients. SIGNIFICANCE STATEMENT: Using a mathematical modeling approach, the quantitative relationships of an orally available anticancer small-molecule enhancer of zeste homolog 2 inhibitor, PF06821497, were characterized among pharmacokinetics, pharmacodynamic biomarker inhibition, and disease responses in nonclinical xenograft models with diffuse large B-cell lymphoma. The modeling results suggest that >70% histone H3 on lysine 27 (H3K27) me3 inhibition would be required for tumor stasis (i.e., 100% tumor growth inhibition). Accordingly, we would propose that an effective concentration for 70% of maximal effect estimate for H3K27me3 inhibition could be considered a minimum target efficacious concentration of PF06821497 in cancer patients.

In silico cancer research towards 3R

BACKGROUND

Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost.

MAIN BODY

We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems.

CONCLUSION

Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

Human microdosing and mice xenograft data of AGM-130 applied to estimate efficacious doses in patients

PURPOSE

AGM-130 is a cyclin-dependent kinase inhibitor that exhibits dose-dependent efficacy in xenograft mouse models. During preclinical pharmacokinetic (PK) studies, mice and rats showed comparable PK parameters while dogs showed unusually high clearance (CL), which has made human PK prediction challenging. To address this discrepancy, we performed a human microdosing PK and developed a mouse PK/PD model in order to guide the first-in-human studies.

METHODS

A microdose of AGM-130 was given via intravenous injection to healthy subjects. Efficacy data obtained using MCF-7 breast cancer cells implanted in mice was analyzed using pre-existing tumor growth inhibition models. We simulated a human PK/PD profile with the PK parameters obtained from the microdose study and the PD parameters estimated from the xenograft PK/PD model.

RESULTS

The human CL of AGM-130 was 3.08 L/h/kg, which was comparable to CL in mice and rats. The time-courses of tumor growth in xenograft model was well described by a preexisting model. Our simulation indicated that the human doses needed for 50 and 90% inhibition of tumor growth were about 100 and 400 mg, respectively.

CONCLUSIONS

This is the first report of using microdose PK and xenograft PK/PD model to predict efficacious doses before the first-in-human trial in cancer patients. In addition, this work highlights the importance of integration of all of information in PK/PD analysis and illustrates how modeling and simulation can be used to add value in the early stages of drug development.

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model

Approximately 10% of colorectal cancers harbor BRAFV600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.