In silico trial for the assessment of givinostat dose adjustment rules based on the management of key hematological parameters in polycythemia vera patients.

Polycythemia vera (PV) is a chronic myeloproliferative neoplasm characterized by excessive levels of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). Givinostat (ITF2357) is a potent histone-deacetylase inhibitor that showed a good safety/efficacy profile in PV patients during phase I/II studies. A phase III clinical trial had been planned and an adaptive dosing protocol had been proposed where givinostat dose is iteratively adjusted every 28 days (one cycle) based on PLT, WBC, and HCT. As support, a simulation platform to evaluate and refine the proposed givinostat dose adjustment rules was developed. A population pharmacokinetic/pharmacodynamic model predicting the givinostat effects on PLT, WBC, and HCT in PV patients was developed and integrated with a control algorithm implementing the adaptive dosing protocol. Ten in silico trials in ten virtual PV patient populations were simulated 500 times. Considering an eight-treatment cycle horizon, reducing/increasing the givinostat daily dose by 25 mg/day step resulted in a higher percentage of patients with a complete hematological response (CHR), that is, PLT ≤400 × 109 /L, WBC ≤10 × 109 /L, and HCT < 45% without phlebotomies in the last three cycles, and a lower percentage of patients with grade II toxicity events compared with 50 mg/day adjustment steps. After the eighth cycle, 85% of patients were predicted to receive a dose ≥100 mg/day and 40.90% (95% prediction interval = [34, 48.05]) to show a CHR. These results were confirmed at the end of 12th, 18th, and 24th cycles, showing a stability of the response between the eighth and 24th cycles.

Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment.

In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.

Model-based prediction of effective target exposure for MEN1611 in combination with trastuzumab in HER2-positive advanced or metastatic breast cancer patients.

MEN1611 is a novel orally bioavailable PI3K inhibitor currently in clinical development for patients with HER2-positive (HER2+) PI3KCA mutated advanced/metastatic breast cancer (BC) in combination with trastuzumab (TZB). In this work, a translational model-based approach to determine the minimum target exposure of MEN1611 in combination with TZB was applied. First, pharmacokinetic (PK) models for MEN1611 and TZB in mice were developed. Then, in vivo tumor growth inhibition (TGI) data from seven combination studies in mice xenograft models representative of the human HER2+ BC non-responsive to TZB (alterations of the PI3K/AkT/mTOR pathway) were analyzed using a PK-pharmacodynamic (PD) TGI model for co-administration of MEN1611 and TZB. The established PK-PD relationship was used to quantify the minimum effective MEN1611 concentration, as a function of TZB concentration, needed for tumor eradication in xenograft mice. Finally, a range of minimum effective exposures for MEN1611 were extrapolated to patients with BC, considering the typical steady-state TZB plasma levels in patients with BC following three alternative regimens (i.v. 4 mg/kg loading dose +2 mg/kg q1w, i.v. 8 mg/kg loading dose +6 mg/kg q3w or s.c. 600 mg q3w). A threshold of about 2000 ng·h/ml for MEN1611 exposure associated with a high likelihood of effective antitumor activity in a large majority of patients was identified for the 3-weekly and the weekly i.v. schedule for TZB. A slightly lower exposure (i.e., 25% lower) was found for the 3-weekly s.c. schedule. This important outcome confirmed the adequacy of the therapeutic dose administered in the ongoing phase 1b B-PRECISE-01 study in patients with HER2+ PI3KCA mutated advanced/metastatic BC.

Modeling restoration of gefitinib efficacy by co-administration of MET inhibitors in an EGFR inhibitor-resistant NSCLC xenograft model: A tumor-in-host DEB-based approach.

MET receptor tyrosine kinase inhibitors (TKIs) can restore sensitivity to gefitinib, a TKI targeting epidermal growth factor receptor (EGFR), and promote apoptosis in non-small cell lung cancer (NSCLC) models resistant to gefitinib treatment in vitro and in vivo. Several novel MET inhibitors are currently under study in different phases of development. In this work, a novel tumor-in-host modeling approach, based on the Dynamic Energy Budget (DEB) theory, was proposed and successfully applied to the context of poly-targeted combination therapies. The population DEB-based tumor growth inhibition (TGI) model well-described the effect of gefitinib and of two MET inhibitors, capmatinib and S49076, on both tumor growth and host body weight when administered alone or in combination in an NSCLC mice model involving the gefitinib-resistant tumor line HCC827ER1. The introduction of a synergistic effect in the combination DEB-TGI model allowed to capture gefitinib anticancer activity enhanced by the co-administered MET inhibitor, providing also a quantitative evaluation of the synergistic drug interaction. The model-based comparison of the two MET inhibitors highlighted that S49076 exhibited a greater anticancer effect as well as a greater ability in restoring sensitivity to gefitinib than the competitor capmatinib. In summary, the DEB-based tumor-in-host framework proposed here can be applied to routine combination xenograft experiments, providing an assessment of drug interactions and contributing to rank investigated compounds and to select the optimal combinations, based on both tumor and host body weight dynamics. Thus, the combination tumor-in-host DEB-TGI model can be considered a useful tool in the preclinical development and a significant advance toward better characterization of combination therapies.

Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules.

Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure-property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS0) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS0 value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict logS0; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity.

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

In Vitro-In Vivo Correlation (IVIVC) Population Modeling for the In Silico Bioequivalence of a Long-Acting Release Formulation of Progesterone.

Health authorities carefully evaluate any change in the batch manufacturing process of a drug before and after regulatory approval. In the absence of an adequate in vitro-in vivo correlation (Level A IVIVC), an in vivo bioequivalence (BE) study is frequently required, increasing the cost and time of drug development. This study focused on developing a Level A IVIVC for progesterone vaginal rings (PVRs), a dosage form designed for the continuous delivery in vivo. The pharmacokinetics (PK) of four batches of rings charged with 125, 375, 750 and 1500 mg of progesterone and characterized by different in vitro release rates were evaluated in two clinical studies. In vivo serum concentrations and in vitro release profiles were used to develop a population IVIVC progesterone ring (P-ring) model through a direct differential-equation-based method and a nonlinear-mixed-effect approach. The in vivo release, Rvivo(t), was predicted from the in vitro profile through a nonlinear relationship. Rvivo(t) was used as the input of a compartmental PK model describing the in vivo serum concentration dynamics of progesterone. The proposed IVIVC P-ring model was able to correctly predict the in vivo concentration-time profiles of progesterone starting from the in vitro PVR release profiles. Its internal and external predictability was carefully evaluated considering the FDA acceptance criteria for IVIVC assessment of extended-release oral drugs. Obtained results justified the use of the in vitro release testing in lieu of clinical studies for the BE assessment of any new PVRs batches. Finally, the possible use of the developed population IVIVC model as a simulator of virtual BE trials was explored through a case study.

A Tumor-in-Host DEB-Based Approach for Modeling Cachexia and Bevacizumab Resistance.

Adequate energy intake and homeostasis are fundamental for the appropriate growth and maintenance of an organism; the presence of a tumor can break this equilibrium. Tumor energy requests can lead to extreme weight loss in animals and cachexia in cancer patients. Angiogenesis inhibitors, acting on tumor vascularization, counteract this tumor-host energy imbalance, with significant results in preclinical models and more limited results in the clinic. Current pharmacokinetic-pharmacodynamic models mainly focus on the antiangiogenic effects on tumor growth but do not provide information about host conditions. A model that can predict energetic conditions that provide significant tumor growth inhibition with acceptable host body weight reduction is therefore needed. We developed a new tumor-in-host dynamic energy budget (DEB)-based model to account for the cytostatic activity of antiangiogenic treatments. Drug effect was implemented as an inhibition of the energy fraction subtracted from the host by the tumor. The model was tested on seven xenograft experiments involving bevacizumab and three different tumor cell lines. The model successfully predicted tumor and host body growth data, providing a quantitative measurement of drug potency and tumor-related cachexia. The inclusion of a hypoxia-triggered resistance mechanism enabled investigation of the decreased efficacy frequently observed with prolonged bevacizumab treatments. In conclusion, the tumor-in-host DEB-based approach has been extended to account for the effect of bevacizumab. The resistance model predicts the response to different administration protocols and, for the first time, the impact of tumor-related cachexia in different cell lines. Finally, the physiologic base of the model strongly suggests its use in translational human research. SIGNIFICANCE: A mathematical model describes tumor growth in animal models, taking into consideration the energy balance involving both the growth of tumor and the physiologic functions of the host.