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.

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).

A Population Pharmacokinetic Model of Macitentan and Its Active Metabolite Aprocitentan in Healthy Volunteers and Patients with Pulmonary Arterial Hypertension.

BACKGROUND: Macitentan and its active metabolite, aprocitentan, are non-peptide, potent, dual endothelin receptor antagonists. Macitentan is approved for the treatment of pulmonary arterial hypertension in adults, at a dose of 10 mg/day. OBJECTIVE: The objective of this study was to develop a comprehensive population model to describe the pharmacokinetics of macitentan and aprocitentan in healthy adults and adult subjects with pulmonary arterial hypertension. METHODS: Pharmacokinetic data of 452 subjects in nine studies, after single and repeated doses (dose range 0.2-600 mg), were pooled for a non-linear mixed-effects analysis and the assessment of covariates, i.e., body weight, age, sex, race, renal and hepatic impairment, health status (healthy volunteers vs patients with pulmonary arterial hypertension), and formulation (capsules vs tablets) on pharmacokinetic parameters. RESULTS: The final model was an open one-compartment disposition model, with linear elimination for macitentan and linear formation and elimination for aprocitentan. A semi-mechanistic absorption model described the dose dependency and multiple peaks observed for macitentan. For a female patient with pulmonary arterial hypertension after oral administration at 10 mg, macitentan reached a maximum concentration after 9 h and, following daily dosing, reached steady state after 3 days with a twofold accumulation factor. The apparent volume of distribution was 34 L and clearance was 1.39 L/h. Aprocitentan reached maximum concentration after 51 h and steady state after 9 days, with a 12.5-fold accumulation factor. Body weight, sex, race, renal impairment, health status, and formulation were statistically significant covariates on pharmacokinetic parameters. CONCLUSIONS: The comprehensive population pharmacokinetic model adequately described the pharmacokinetics of macitentan and aprocitentan across different dose concentrations, regimens, and formulations. Several covariates significantly influenced the pharmacokinetics of macitentan and aprocitentan, but none was considered clinically relevant.

XPG mRNA expression levels modulate prognosis in resected non-small-cell lung cancer in conjunction with BRCA1 and ERCC1 expression.

BACKGROUND: Molecular markers can help identify patients with early-stage non-small-cell lung cancer (NSCLC) with a high risk of relapse. Excision repair cross-complementing 1 (ERCC1), Xeroderma pigmentosum group G (XPG), and breast cancer 1 (BRCA1) are involved in DNA damage repair, whereas ribonucleotide reductase M1 (RRM1) is implicated in DNA synthesis. Expression levels of these molecules might therefore have a prognostic role in lung cancer. PATIENTS AND METHODS: We examined ERCC1, RRM1, XPG, and BRCA1 mRNA levels by real-time quantitative polymerase chain reaction in 54 patients with stage IB-IIB resected NSCLC. A strong correlation was observed between the 4 genes. RESULTS: For patients with low BRCA1, regardless of XPG mRNA expression levels, disease-free survival (DFS) was not reached. For patients with intermediate/high BRCA1 and high XPG, DFS was 50.7 months. However, for patients with intermediate/high BRCA1 and low/intermediate XPG, DFS decreased to 16.3 months (P = .002). Similar differences were observed in overall survival, with median survival not reached for patients with low BRCA1, regardless of XPG levels, or for patients with intermediate/high BRCA1 and high XPG. Conversely, for patients with intermediate/high BRCA1 levels and low/intermediate XPG levels, median survival dropped to 25.5 months (P = .007). CONCLUSION: BRCA1 and XPG were identified as independent prognostic factors for both median survival and DFS. High BRCA1 mRNA expression confers poor prognosis in early NSCLC, and the combination of high BRCA1 and low XPG expression still further increases the risk of shorter survival. These findings can help optimize the customization of adjuvant chemotherapy.

BRCA1: a novel prognostic factor in resected non-small-cell lung cancer.

BACKGROUND: Although early-stage non-small-cell lung cancer (NSCLC) is considered a potentially curable disease following complete resection, patients have a wide spectrum of survival according to stage (IB, II, IIIA). Within each stage, gene expression profiles can identify patients with a higher risk of recurrence. We hypothesized that altered mRNA expression in nine genes could help to predict disease outcome: excision repair cross-complementing 1 (ERCC1), myeloid zinc finger 1 (MZF1) and Twist1 (which regulate N-cadherin expression), ribonucleotide reductase subunit M1 (RRM1), thioredoxin-1 (TRX1), tyrosyl-DNA phosphodiesterase (Tdp1), nuclear factor of activated T cells (NFAT), BRCA1, and the human homolog of yeast budding uninhibited by benzimidazole (BubR1). METHODOLOGY AND PRINCIPAL FINDINGS: We performed real-time quantitative polymerase chain reaction (RT-QPCR) in frozen lung cancer tissue specimens from 126 chemonaive NSCLC patients who had undergone surgical resection and evaluated the association between gene expression levels and survival. For validation, we used paraffin-embedded specimens from 58 other NSCLC patients. A strong inter-gene correlation was observed between expression levels of all genes except NFAT. A Cox proportional hazards model indicated that along with disease stage, BRCA1 mRNA expression significantly correlated with overall survival (hazard ratio [HR], 1.98 [95% confidence interval (CI), 1.11-6]; P = 0.02). In the independent cohort of 58 patients, BRCA1 mRNA expression also significantly correlated with survival (HR, 2.4 [95%CI, 1.01-5.92]; P = 0.04). CONCLUSIONS: Overexpression of BRCA1 mRNA was strongly associated with poor survival in NSCLC patients, and the validation of this finding in an independent data set further strengthened this association. Since BRCA1 mRNA expression has previously been linked to differential sensitivity to cisplatin and antimicrotubule drugs, BRCA1 mRNA expression may provide additional information for customizing adjuvant antimicrotubule-based chemotherapy, especially in stage IB, where the role of adjuvant chemotherapy has not been clearly demonstrated.