Translational PK/PD Modeling of Tumor Growth Inhibition and Target Inhibition to Support Dose Range Selection of the LMP7 Inhibitor M3258 in Relapsed/Refractory Multiple Myeloma.

M3258 is an orally bioavailable, potent, selective, reversible inhibitor of the large multifunctional peptidase 7 (LMP7, ß5i, PSMB8) proteolytic subunit of the immunoproteasome, a component of the cellular protein degradation machinery, highly expressed in malignant hematopoietic cells including multiple myeloma. Here we describe the fit-for-purpose pharmacokinetic (PK)/pharmacodynamic (PD)/efficacy modeling of M3258 based on preclinical data from several species. The inhibition of LMP7 activity (PD) and tumor growth (efficacy) were tested in human multiple myeloma xenografts in mice. PK and efficacy data were correlated yielding a free M3258 concentration of 45 nM for half-maximal tumor growth inhibition (KC50). As M3258 only weakly inhibits LMP7 in mouse cells, both in vitro and in vivo bridging studies were performed in rats, monkeys, and dogs for translational modeling. These data indicated that the PD response in human xenograft models was closely reflected in dog PBMCs. A PK/PD model was established, predicting a free IC50 value of 9 nM for M3258 in dogs in vivo, in close agreement with in vitro measurements. In parallel, the human PK parameters of M3258 were predicted by various approaches including in vitro extrapolation and allometric scaling. Using PK/PD/efficacy simulations, the efficacious dose range and corresponding PD response in human were predicted. Taken together, these efforts supported the design of a phase Ia study of M3258 in multiple myeloma patients (NCT04075721). At the lowest tested dose level, the predicted exposure matched well with the observed exposure while the duration of LMP7 inhibition was underpredicted by the model. SIGNIFICANCE STATEMENT: M3258 is a novel inhibitor of the immunoproteasome subunit LMP7. The human PK and human efficacious dose range of M3258 were predicted using in vitro-in vivo extrapolation and allometric scaling methods together with a fit-for-purpose PK/PD and efficacy model based on data from several species. A comparison with data from the Phase Ia clinical study showed that the human PK was accurately predicted, while the extent and duration of PD response were more pronounced than estimated.

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology.

BACKGROUND: To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data. METHODS: We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves. RESULTS: The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication. CONCLUSIONS: A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

Preclinical Pharmacokinetics and Translational Pharmacokinetic/Pharmacodynamic Modeling of M8891, a Potent and Reversible Inhibitor of Methionine Aminopeptidase 2.

INTRODUCTION: M8891 is a selective and reversible inhibitor of methionine aminopeptidase 2 (MetAP2). We describe translational research to define the target pharmacokinetics (PK) of M8891 and associated pharmacodynamic (PD) levels, which were used to support efficacious dose selection in humans. METHODS: In vitro and in vivo PK characteristics were investigated in animal species, and data integrated using in vitro-in vivo correlation and allometric methods to predict the clearance, volume of distribution, and absorption parameters of M8891 in humans. In parallel, inhibition of MetAP2 activity by M8891 was studied in renal cancer xenografts in mice by measuring accumulation of Met-EF1α, a substrate of MetAP2. The corresponding PD effect was described by a turnover and effect compartment model. This model was used to simulate PD at the M8891 dose showing in vivo efficacy, i.e. significant tumor growth inhibition. Simulations of M8891 PK and associated PD in humans were conducted by integrating predicted human PK parameters into the preclinical PK/PD model. RESULTS: The target minimum PD level associated with efficacy was determined to be 125 µg Met-EF1α per mg protein. Integrating predicted human PK parameters into the preclinical PK/PD model defined a minimal M8891 concentration at steady-state (Ctrough) of 1500 ng/mL (3.9 µM) in humans as being required to produce the corresponding minimum target Met-EF1a level (125 µg per mg protein). CONCLUSION: The defined target PK and PD levels supported the design of the clinical Phase Ia dose escalation study of M8891 (NCT03138538) and selection of the recommended Phase II dose.

An innovative study design with intermittent dosing to generate a GLP-regulatory package in preclinical species for long lasting molecule M5717, inhibitor of Plasmodium eukaryotic translation elongation factor 2.

M5717 is a novel drug inhibiting synthesis of elongation factor 2 (PeEF2) in Plasmodium species, showing potent anti-malarial activity in preclinical studies. Traditional daily-dosing animal experiments estimating maximum safe starting dose for a first-in-human study ('no observed adverse effect level'; NOAEL) were unsuccessful due to the long pharmacokinetic half-life of M5717, causing significant drug accumulation and high exposure. This study describes an innovative strategy to produce a GLP-certified toxicology package and estimate NOAEL for long-lasting molecules like M5717. Simulated pharmacokinetic/toxicokinetic profiles were used to design the dosing schedule for preclinical safety studies and to determine the 14-day total exposure. Animals (rats/dogs) were administered various doses of M5717 using an intermittent dosing schedule allowing partial drug elimination and alleviation of toxicity during off-treatment days to maintain a minimal parasitical concentration (MPC) of 10 ng/mL; subsequently animals were monitored for toxicity and mortality. Results showed good correlation to the modelled data used to design the dosing regimen and required MPC was reached for M5717 in study animals and could be used to calculate NOAEL. This fit-for-purpose study design allowed for maintaining clinically relevant exposure to M5717, whilst minimizing toxicity-causing compound accumulation, an aspect unaddressed by traditional NOAEL-estimating experiments. This is the first time that a compound-specific, species-specific, kinetic model-based approach to preclinical study design for regulatory toxicology studies has been described and applied to an antimalarial drug candidate with long pharmacokinetic half-life. It has potential for application to other drugs with long half-lives, supporting their clinical development.

Exposure-response modeling improves selection of radiation and radiosensitizer combinations.

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

Insights into Praziquantel Metabolism and Potential Enantiomeric Cytochrome P450-Mediated Drug-Drug Interaction.

The active enantiomer R-Praziquantel (PZQ) shows a clinically lower relative exposure when administered enantiomerically pure compared with a racemic form. We investigated the hypothesis that enantiomer-enantiomer interactions on cytochrome P450 (P450) enzymes could explain this observation and aimed to further deepen the understanding of PZQ metabolism. First, in an in vitro metabolite profiling study, the formation of multiple metabolites per P450, together with an observed interconversion of cis-4'-OH-PZQ to trans-4'-OH-PZQ in human hepatocytes, pointed out the inadequacy of measuring metabolite formation in kinetic studies. Thus, a substrate depletion approach to study PZQ enantiomeric interactions was applied. Second, an abundant CYP3A4 metabolite found in previous studies was structurally characterized. Third, substrate depletion methodologies were applied to determine P450 enzyme kinetics of PZQ and to further estimate enantiomer-enantiomer inhibitory parameters. A competitive inhibition between PZQ enantiomers for CYP2C9, 2C19, 3A4, and 3A5 was revealed. Analyses considering the clearance of only one or both enantiomers provided comparable enantiomer-enantiomer inhibition estimates. To conclude, this paper provides new insights into PZQ metabolic profile to enable a better understanding of enantioselective pharmacokinetics using substrate depletion-based methods. SIGNIFICANCE STATEMENT: In this study, enantiomer-enantiomer interactions of praziquantel on cytochrome P450 metabolizing enzymes are investigated via substrate depletion measurement using modeling methods. Together with new insights into the praziquantel metabolism, this work provides a novel data set to understand its pharmacokinetics.

Characterization of Pharmacokinetics in the Göttingen Minipig with Reference Human Drugs: An In Vitro and In Vivo Approach.

PURPOSE: This study aims to expand our understanding of the mechanisms of drug absorption, distribution, metabolism and excretion in the Göttingen minipig to aid a knowledge-driven selection of the optimal species for preclinical pharmaceutical research. METHODS: The pharmacokinetics of seven reference compounds (antipyrine, atenolol, cimetidine, diazepam, hydrochlorothiazide, midazolam and theophylline) was investigated after intravenous and oral dosing in minipigs. Supportive in vitro data were generated on hepatocellularity, metabolic clearance in hepatocytes, blood cell and plasma protein binding and metabolism routes. RESULTS: Systemic plasma clearance for the seven drugs ranged from low (1.1 ml/min/kg, theophylline) to close to liver blood flow (37.4 ml/min/kg, cimetidine). Volume of distribution in minipigs ranged from 0.7 L/kg for antipyrine to 3.2 L/kg for hydrochlorothiazide. A gender-related difference of in vivo metabolic clearance was observed for antipyrine. The hepatocellularity for minipig was determined as 124 Mcells/g liver, similar to the values reported for human. Based on these data a preliminary in vitro to in vivo correlation (IVIVC) for metabolic clearance measured in hepatocytes was investigated. Metabolite profiles of diazepam and midazolam compared well between minipig and human. CONCLUSIONS: The results of the present study support the use of in vitro metabolism data for the evaluation of minipig in preclinical research and safety testing.

A First-in-human, Dose-escalation Study of the Methionine Aminopeptidase 2 Inhibitor M8891 in Patients with Advanced Solid Tumors.

Methionine aminopeptidase 2 (MetAP2) is essential to endothelial cell growth and proliferation during tumor angiogenesis. M8891 is a novel orally bioavailable, potent, selective, reversible MetAP2 inhibitor with antiangiogenic and antitumor activity in preclinical studies. The safety, tolerability, pharmacokinetics, and pharmacodynamics of M8891 monotherapy were assessed in a phase I, first-in-human, multicenter, open-label, single-arm, dose-escalation study (NCT03138538). Patients with advanced solid tumors received 7-80 mg M8891 once daily in 21-day cycles. The primary endpoint was dose-limiting toxicity (DLT) during cycle 1, with the aim to determine the maximum tolerated dose (MTD). Twenty-seven patients were enrolled across six dose levels. Two DLTs (platelet count decrease) were reported, one each at 60 and 80 mg/once daily M8891, resolving after treatment discontinuation. MTD was not determined. The most common treatment-emergent adverse event was platelet count decrease. M8891 plasma concentration showed dose-linear increase up to 35 mg and low-to-moderate variability; dose-dependent tumor accumulation of methionylated elongation factor 1α, a MetAP2 substrate, was observed, demonstrating MetAP2 inhibition. Pharmacokinetic/pharmacodynamic response data showed that preclinically defined target levels required for in vivo efficacy were achieved at safe, tolerated doses. Seven patients (25.9%) had stable disease for 42-123 days. We conclude that M8891 demonstrates a manageable safety profile, with dose-proportional exposure and low-to-moderate interpatient variability at target pharmacokinetic/pharmacodynamic levels at ≤35 mg M8891 once daily. On the basis of the data, 35 mg M8891 once daily is the recommended phase II dose for M8891 monotherapy. This study forms the basis for future development of M8891 in monotherapy and combination studies. Significance: M8891 represents a novel class of reversible MetAP2 inhibitors and has demonstrated preclinical antitumor activity. This dose-escalation study assessed M8891 treatment for patients with advanced solid tumors. M8891 demonstrated favorable pharmacokinetics, tumoral target engagement, and a manageable safety profile, and thus represents a novel antitumor strategy warranting further clinical studies.

Optimized scaling of translational factors in oncology: from xenografts to RECIST.

PURPOSE: Tumor growth inhibition (TGI) models are regularly used to quantify the PK-PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response. METHOD: To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data. RESULTS: The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of - 0.25. CONCLUSIONS: We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.

Using thrombocytopenia modeling to investigate the mechanisms underlying platelet depletion induced by pan-proteasome inhibitors.

Pan-proteasome inhibitors (pPIs) significantly improve outcomes in patients with multiple myeloma; however, their indiscriminate inhibition of multiple proteasome and immunoproteasome subunits causes diverse toxicities, including thrombocytopenia. We investigated the mechanisms underlying the platelet depletion induced by the pPIs bortezomib, carfilzomib, and ixazomib. An established thrombocytopenia model was adapted for each compound (bortezomib, ixazomib, and carfilzomib) to compare the following two pharmacodynamic mechanisms: a reversible inhibition of new progenitor cell formation (the myelosuppression model) and a reversible effect on the function of megakaryocytes to bud new platelets (platelet formation model). Bortezomib, ixazomib, and carfilzomib plasma concentration profiles and platelet counts were extracted from the literature. Pharmacokinetic (PK) and thrombocytopenia models were developed to predict the PK of these drugs and to describe their effects on proliferating cells and platelet budding. The PK models reproduced the exposure of the three compounds at steady state well compared with those reported in the literature. Both the platelet formation and myelosuppression models seemed able to describe the platelet depletion caused by bortezomib, ixazomib, and carfilzomib. Estimated structural parameters in the myelosuppression model were in the range of the values reported in the literature, whereas the mean transit time estimated with the platelet formation model was 3-fold to 10-fold higher than the highest reported value. The model of drug-induced myelosuppression yielded estimates of structural parameters in the range of those previously reported. The platelet formation model captured the temporal variation reported in clinical studies.

Modeling long-term tumor growth and kill after combinations of radiation and radiosensitizing agents.

PURPOSE: Radiation therapy, whether given alone or in combination with chemical agents, is one of the cornerstones of oncology. We develop a quantitative model that describes tumor growth during and after treatment with radiation and radiosensitizing agents. The model also describes long-term treatment effects including tumor regrowth and eradication. METHODS: We challenge the model with data from a xenograft study using a clinically relevant administration schedule and use a mixed-effects approach for model-fitting. We use the calibrated model to predict exposure combinations that result in tumor eradication using Tumor Static Exposure (TSE). RESULTS: The model is able to adequately describe data from all treatment groups, with the parameter estimates taking biologically reasonable values. Using TSE, we predict the total radiation dose necessary for tumor eradication to be 110 Gy, which is reduced to 80 or 30 Gy with co-administration of 25 or 100 mg kg-1 of a radiosensitizer. TSE is also explored via a heat map of different growth and shrinkage rates. Finally, we discuss the translational potential of the model and TSE concept to humans. CONCLUSIONS: The new model is capable of describing different tumor dynamics including tumor eradication and tumor regrowth with different rates, and can be calibrated using data from standard xenograft experiments. TSE and related concepts can be used to predict tumor shrinkage and eradication, and have the potential to guide new experiments and support translations from animals to humans.

The role of quantitative ADME proteomics to support construction of physiologically based pharmacokinetic models for use in small molecule drug development.

Pharmacokinetics (PK) refers to the time course of drug concentrations in the body and since knowledge of PK aids understanding of drug efficacy and safety, numerous PK studies are performed in animals and humans during the drug development process. In vitro to in vivo extrapolation and physiologically based pharmacokinetic (PBPK) modeling are tools that integrate data from various in silico, in vitro, and in vivo sources to deliver mechanistic quantitative simulations of in vivo PK. PBPK models are used to predict human PK and to evaluate the effects of intrinsic factors such as organ dysfunction, age, and genetics as well as extrinsic factors such as co-administered drugs. In recent years, the use of PBPK within the industry has greatly increased. However, insufficient data on how the abundance of metabolic enzymes and membrane transporters vary in different human patient populations and in different species has been a limitation. A major advance is therefore expected through reliable quantification of the abundance of these proteins in tissues. This review describes the role of PBPK modeling in drug discovery and development, outlines the assumptions involved in integrating protein abundance data, and describes the advances made and expected in determining abundance of relevant proteins through mass spectrometric techniques.

A structural model of the VEGF signalling pathway: emergence of robustness and redundancy properties.

The vascular endothelial growth factor (VEGF) is known as one of the main promoter of angiogenesis - the process of blood vessel formation. Angiogenesis has been recognized as a key stage for cancer development and metastasis. In this paper, we propose a structural model of the main molecular pathways involved in the endothelial cells response to VEGF stimuli. The model, built on qualitative information from knowledge databases, is composed of 38 ordinary differential equations with 78 parameters and focuses on the signalling driving endothelial cell proliferation, migration and resistance to apoptosis. Following a VEGF stimulus, the model predicts an increase of proliferation and migration capability, and a decrease in the apoptosis activity. Model simulations and sensitivity analysis highlight the emergence of robustness and redundancy properties of the pathway. If further calibrated and validated, this model could serve as tool to analyse and formulate new hypothesis on th e VEGF signalling cascade and its role in cancer development and treatment.