Human Safety, Tolerability, Pharmacokinetics, and Pharmacodynamics of ASC42, a Novel Farnesoid X Receptor Agonist.

BACKGROUND: ASC42 is a non-steroidal farnesoid X receptor agonist currently in clinical development for chronic liver diseases, such as nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) and primary biliary cirrhosis (PBC). OBJECTIVE: The objective of this study was to assess the safety, pharmacokinetics (PK), and pharmacodynamics (PD) of ASC42 in healthy subjects. METHODS: We conducted the first-in-human study of ASC42 following single and multiple ascending doses (SAD/MAD) and food effect in healthy subjects. The SAD study included five cohorts receiving 5-200 mg ASC42 or placebo and one cohort that was given 15 mg ASC42 with a high-fat meal. The MAD study included three cohorts receiving 5-50 mg ASC42 or placebo once-daily (QD) for 14 days. RESULTS: A total of 65 healthy subjects were enrolled and one subject in the MAD study (cohort 8, ASC42 50 mg) withdrew from the study due to an unrelated serious adverse event (SAE) of atrial fibrillation. Pruritus was observed at the highest doses (200 mg cohort in SAD and 50 mg cohort in MAD). Most AEs were mild or moderate. No life-threatening or fatal AEs occurred. ASC42 showed a proportional increase in exposure and elimination half-life following both single and multiple dosing. There was a 21% and 37% decrease in area under the curve (AUC) and maximum plasma concentration (Cmax) when ASC42 was coadministered with food. The steady state was reached on day 4 with a mild accumulation (1.02-1.74-fold). ASC42 showed dose-dependent increases in fibroblast growth factor 19 and decreases in 7α-hydroxy-4-cholesten-3-one. Cholesterol remained within normal limits during study. CONCLUSION: ASC42 was well tolerated with a pharmacokinetic profile suitable for QD dosing, and demonstrated dose-dependent targets engagement without altering plasma cholesterol in healthy subjects. TRIAL REGISTRATION NUMBER: NCT04679129.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies.

Oral drug absorption is a complex process depending on many factors, including the physicochemical properties of the drug, formulation characteristics and their interplay with gastrointestinal physiology and biology. Physiological-based pharmacokinetic (PBPK) models integrate all available information on gastro-intestinal system with drug and formulation data to predict oral drug absorption. The latter together with in vitro-in vivo extrapolation and other preclinical data on drug disposition can be used to predict plasma concentration-time profiles in silico. Despite recent successes of PBPK in many areas of drug development, an improvement in their utility for evaluating oral absorption is much needed. Current status of predictive performance, within the confinement of commonly available in vitro data on drugs and formulations alongside systems information, were tested using 3 PBPK software packages (GI-Sim (ver.4.1), Simcyp® Simulator (ver.15.0.86.0), and GastroPlus™ (ver.9.0.00xx)). This was part of the Innovative Medicines Initiative (IMI) Oral Biopharmaceutics Tools (OrBiTo) project. Fifty eight active pharmaceutical ingredients (APIs) were qualified from the OrBiTo database to be part of the investigation based on a priori set criteria on availability of minimum necessary information to allow modelling exercise. The set entailed over 200 human clinical studies with over 700 study arms. These were simulated using input parameters which had been harmonised by a panel of experts across different software packages prior to conduct of any simulation. Overall prediction performance and software packages comparison were evaluated based on performance indicators (Fold error (FE), Average fold error (AFE) and absolute average fold error (AAFE)) of pharmacokinetic (PK) parameters. On average, PK parameters (Area Under the Concentration-time curve (AUC0-tlast), Maximal concentration (Cmax), half-life (t1/2)) were predicted with AFE values between 1.11 and 1.97. Variability in FEs of these PK parameters was relatively high with AAFE values ranging from 2.08 to 2.74. Around half of the simulations were within the 2-fold error for AUC0-tlast and around 90% of the simulations were within 10-fold error for AUC0-tlast. Oral bioavailability (Foral) predictions, which were limited to 19 APIs having intravenous (i.v.) human data, showed AFE and AAFE of values 1.37 and 1.75 respectively. Across different APIs, AFE of AUC0-tlast predictions were between 0.22 and 22.76 with 70% of the APIs showing an AFE > 1. When compared across different formulations and routes of administration, AUC0-tlast for oral controlled release and i.v. administration were better predicted than that for oral immediate release formulations. Average predictive performance did not clearly differ between software packages but some APIs showed a high level of variability in predictive performance across different software packages. This variability could be related to several factors such as compound specific properties, the quality and availability of information, and errors in scaling from in vitro and preclinical in vivo data to human in vivo behaviour which will be explored further. Results were compared with previous similar exercise when the input data selection was carried by the modeller rather than a panel of experts on each in vitro test. Overall, average predictive performance was increased as reflected in smaller AAFE value of 2.8 as compared to AAFE value of 3.8 in case of previous exercise.

Ribociclib Drug-Drug Interactions: Clinical Evaluations and Physiologically-Based Pharmacokinetic Modeling to Guide Drug Labeling.

Ribociclib is approved in combination with endocrine therapy as initial endocrine-based therapy for HR-positive and HER2-negative advanced breast cancer. Ribociclib is primarily metabolized by CYP3A4 and, in vitro, is an inhibitor of CYP3A and CYP1A2. Ritonavir (a strong CYP3A inhibitor) increased ribociclib 400 mg single-dose area under the plasma concentration-time curve (AUC) by 3.2-fold, whereas rifampin (a strong CYP3A inducer) decreased ribociclib AUC by 89% in healthy volunteers (HVs). Multiple 400 mg ribociclib doses increased midazolam (CYP3A substrate) AUC by 3.8-fold and caffeine (CYP1A2 substrate) AUC by 1.2-fold vs. each agent alone. A physiologically-based pharmacokinetic (PBPK) model was developed integrating in vitro, preclinical, and clinical data of HVs and patients with cancer. Data predictions indicated that multiple 600 mg ribociclib doses increased midazolam AUC by 5.85-fold and ritonavir increased ribociclib 600 mg multiple dose AUC by 1.31-fold in cancer patients. Based on pharmacokinetics, safety, and efficacy data, and PBPK modeling, dosing modifications for ribociclib recommend avoiding concurrent use of strong CYP3A inhibitors/inducers, and caution regarding using CYP3A substrates with narrow therapeutic indices.

Model-Informed Drug Development for Everolimus Dosing Selection in Pediatric Infant Patients.

Everolimus is currently approved in Europe as an adjunctive therapy for patients aged ≥ 2 years with tuberous sclerosis complex (TSC)-associated treatment-refractory partial-onset seizures, based on the EXIST-3 study (NCT01713946) results. As TSC-associated seizures can also affect children aged between 6 months and 2 years, a modeling and simulation (M&S) approach was undertaken to extrapolate exposure (trough plasma concentration (Cmin )) after a dose of 6 mg/m2 and reduction in seizure frequency (RSF). A physiologically based pharmacokinetic model using Simcyp was developed to predict Cmin in adult and pediatric patients, which was then used by a population pharmacodynamic model and a linear mixed effect model to predict short-term and long-term efficacy in adults (for validation) and in children, respectively. Based on the results of the M&S study, everolimus at the dose of 6 mg/m2 is anticipated to be an efficacious treatment in children 6 months to 2 years of age (up to 77.8% RSF) with concentrations within the recommended target range.

ICH M9 Guideline in Development on Biopharmaceutics Classification System-Based Biowaivers: An Industrial Perspective from the IQ Consortium.

In October 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) ICH began efforts to provide recommendations to harmonize guidances for biopharmaceutics classification system (BCS)-based biowaivers. Topics to be addressed included consideration of the dose used to classify solubility, tests, and criteria for establishing highly permeable, dissolution conditions, the influence of excipients, and aspects of product strength. The International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) is a technically focused organization of pharmaceutical and biotechnology companies with a mission of advancing science and technology to augment the capability of member companies to develop transformational solutions that benefit patients, regulators, and the broader R&D community. Its members have substantial expertise in all scientific domains associated with BCS-based waivers and drug product quality, as well as considerable experience in the application of BCS-based biowaivers. The ICH process recognizes that harmonization is achieved through the development of guidelines via a process of scientific consensus with regulatory and industry experts working side-by-side. Thus, to facilitate these efforts and to encourage open and transparent discussion of other perspectives that may exist, IQ offers their perspective on these and related topics.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities.

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Prediction of the Impact of Cytochrome P450 2C9 Genotypes on the Drug-Drug Interaction Potential of Siponimod With Physiologically-Based Pharmacokinetic Modeling: A Comprehensive Approach for Drug Label Recommendations.

We predicted the drug-drug interaction (DDI) potential of siponimod in presence of cytochrome P450 (CYP)2C9/CYP3A4 inhibitors/inducers in subjects with different CYP2C9 genotypes by physiologically-based pharmacokinetic (PK) modeling. The model was established using in vitro and clinical PK data and verified by adequately predicting siponimod PK when coadministered with rifampin. With strong and moderate CYP3A4 inhibitors, an increased DDI risk for siponimod was predicted for CYP2C9*3/*3 genotype vs. other genotypes area under the curve ratio (AUCR): 3.03-4.20 vs. ≤ 1.49 for strong; 2.42 vs. 1.14-1.30 for moderate. AUCRs increased with moderate (2.13-2.49) and weak (1.12-1.42) CYP3A4/CYP2C9 inhibitors to the same extent for all genotypes. With strong CYP3A4/moderate CYP2C9 inducers and moderate CYP3A4 inducers, predicted AUCRs were 0.21-0.32 and 0.35-0.71, respectively. This complementary analysis to the clinical PK-DDI studies confirmed the relevant influence of CYP2C9 polymorphism on the DDI behavior of siponimod and represented the basis for the DDI labeling recommendations.

Drug-drug interaction (DDI) assessments of ruxolitinib, a dual substrate of CYP3A4 and CYP2C9, using a verified physiologically based pharmacokinetic (PBPK) model to support regulatory submissions.

Ruxolitinib is mainly metabolized by cytochrome P450 (CYP) enzymes CYP3A4 and CYP2C9 followed by minor contributions of other hepatic CYP enzymes in vitro. A physiologically based pharmacokinetic (PBPK) model was established to evaluate the changes in the ruxolitinib systemic exposures with co-administration of CYP3A4 and CYP2C9 perpetrators. The fractions metabolized in the liver via oxidation by CYP enzymes (fm,CYP3A4 = 0.75, fm,CYP2C9 = 0.19, and fm,CYPothers = 0.06) for an initial ruxolitinib model based on in vitro data were optimized (0.43, 0.56, and 0.01, respectively) using the observed exposure changes of ruxolitinib (10 mg) with co-administered ketoconazole (200 mg). The reduced amount of fm,CYP3A4 was distributed to fm,CYP2C9. For the initial ruxolitinib model with co-administration of ketoconazole, the area under the curve (AUC) increase of 2.60-fold was over-estimated compared with the respective observation (1.91-fold). With the optimized fm values, the predicted AUC ratio was 1.82. The estimated AUC ratios of ruxolitinib by co-administration of the moderate CYP3A4 inhibitor erythromycin (500 mg) and the strong CYP3A4 inducer rifampicin (600 mg) were within a 20% error compared with the clinically observed values. The PBPK modeling results may provide information on the labeling, i.e. supporting a dose reduction by half for co-administration of strong CYP3A4 inhibitors. Furthermore, an AUC increase of ruxolitinib in the absence or presence of the dual CYP3A4 and CYP2C9 inhibitor fluconazole (100-400 mg) was prospectively estimated to be 1.94- to 4.31-fold. Fluconazole simulation results were used as a basis for ruxolitinib dose adjustment when co-administering perpetrator drugs. A ruxolitinib PBPK model with optimized fm,CYP3A4 and fm,CYP2C9 was established to evaluate victim DDI risks. The previous minimal PBPK model was supported by the FDA for the dose reduction strategy, halving the dose with the concomitant use of strong CYP3A4 inhibitors and dual inhibitors on CYP2C9 and CYP3A4, such as fluconazole at ≤200 mg. Fluconazole simulation results were used as supportive evidence in discussions with the FDA and EMA about ruxolitinib dose adjustment when co-administering perpetrator drugs. Thus, this study demonstrated that PBPK modeling can support characterizing DDI liabilities to inform the drug label and might help reduce the number of clinical DDI studies by simulations of untested scenarios, when a robust PBPK model is established.

Physiologically Based Pharmacokinetic Modeling to Supplement Nilotinib Pharmacokinetics and Confirm Dose Selection in Pediatric Patients.

In adult patients, nilotinib is indicated for chronic myeloid leukemia at an approved oral dose of 300 or 400 mg BID. Physiologically based pharmacokinetic (PBPK) model was developed to describe and supplement limited PK data in the pediatric population ranging from 2 to less than 6 years of age and ultimately inform dosing regimen. An adult Simcyp PBPK model was established and verified with clinical pharmacokinetic data after a single or multiple oral doses of 400 mg nilotinib (230 mg/m2). The model was then applied to a pediatric PBPK model, taking account of ontogeny profiles of metabolizing enzymes and pediatric physiological parameters. The model was further verified using observed pediatric PK data in 12- to <18-year-old and from 6- to <12-year-old patients. The PBPK models were able to recover, describe, and supplement the limited nilotinib concentration-time data profile in 2- to <6-year-old patients after a single dose and Cmin,ss after BID dosing. The exposure (Cmax,ss, Cmin,ss, and AUCtau,ss) was predicted to be similar across age groups. PBPK model simulations confirmed that body surface area-normalized dosing regimen of 230 mg/m2 is considered appropriate for pediatric patients >2 to <18 years of age.

Clinical Pharmacokinetic and Pharmacodynamic Overview of Nilotinib, a Selective Tyrosine Kinase Inhibitor.

Nilotinib, an oral inhibitor of the tyrosine kinase activity of Abelson protein, is approved for the treatment of patients with newly diagnosed chronic myeloid leukemia (CML) in chronic phase and patients with CML in chronic phase or accelerated phase resistant or intolerant to prior therapies. This review describes the pharmacokinetic and pharmacodynamic data of nilotinib in patients with CML and in healthy volunteers. Nilotinib is rapidly absorbed, with a peak serum concentration approximately 3 hours after dosing. The area under the plasma drug concentration-time curve over 24 hours and the peak serum concentration of nilotinib were dose proportional from 50-400 mg once daily. The metabolism of nilotinib is primarily via hepatic cytochrome P450 (CYP) 3A4 according to in vitro studies. In the clinical setting, exposure to nilotinib was significantly reduced by the induction of CYP3A4 with rifampicin and significantly increased by the inhibition of CYP3A with ketoconazole. Additionally, nilotinib is a competitive inhibitor of CYP3A4/5, CYP2C8, CYP2C9, CYP2D6, and uridine diphosphate glucuronosyltransferase 1A1. The bioavailability of nilotinib is increased by up to 82% when given with a high-fat meal compared with fasted state. There is a positive correlation between the occurrences of all-grade total bilirubin elevations and the steady-state nilotinib trough concentrations. Fredericia method corrected QT interval change from baseline was observed to have a correlation with nilotinib exposure. No significant relationship between nilotinib exposure and major molecular response at 12 months was seen at therapeutic doses of nilotinib 300-400 mg, probably due to the narrow range of the doses investigated.

Verification of a physiologically based pharmacokinetic model of ritonavir to estimate drug-drug interaction potential of CYP3A4 substrates.

Ritonavir is one of several ketoconazole alternatives used to evaluate strong CYP3A4 inhibition potential in clinical drug-drug interaction (DDI) studies. In this study, four physiologically based pharmacokinetic (PBPK) models of ritonavir as an in vivo time-dependent inhibitor of CYP3A4 were created and verified for oral doses of 20, 50, 100 and 200 mg using the fraction absorbed (Fa ) and oral clearance (CLoral ) values reported in the literature, because transporter and CYP enzyme reaction phenotyping data were not available. The models were used subsequently to predict and compare the magnitude of the AUC increase in nine reference DDI studies evaluating the effect of ritonavir at steady-state on midazolam (CYP3A4 substrate) exposure. Midazolam AUC and Cmax ratios were predicted within 2-fold of the respective observations in seven studies. Simulations of the hepatic and gut CYP3A4 abundance after multiple oral dosing of ritonavir indicated that a 3-day treatment with ritonavir 100 mg twice daily is sufficient to reach maximal CYP3A4 inhibition and subsequent systemic exposure increase of a CYP3A4 substrate, resulting in the reliable estimation of fm,CYP3A4 . The ritonavir model was submitted as part of the new drug application for Kisqali® (ribociclib) and accepted by health authorities.

Simultaneous Physiologically Based Pharmacokinetic (PBPK) Modeling of Parent and Active Metabolites to Investigate Complex CYP3A4 Drug-Drug Interaction Potential: A Case Example of Midostaurin.

Midostaurin (PKC412) is being investigated for the treatment of acute myeloid leukemia (AML) and advanced systemic mastocytosis (advSM). It is extensively metabolized by CYP3A4 to form two major active metabolites, CGP52421 and CGP62221. In vitro and clinical drug-drug interaction (DDI) studies indicated that midostaurin and its metabolites are substrates, reversible and time-dependent inhibitors, and inducers of CYP3A4. A simultaneous pharmacokinetic model of parent and active metabolites was initially developed by incorporating data from in vitro, preclinical, and clinical pharmacokinetic studies in healthy volunteers and in patients with AML or advSM. The model reasonably predicted changes in midostaurin exposure after single-dose administration with ketoconazole (a 5.8-fold predicted versus 6.1-fold observed increase) and rifampicin (90% predicted versus 94% observed reduction) as well as changes in midazolam exposure (1.0 predicted versus 1.2 observed ratio) after daily dosing of midostaurin for 4 days. The qualified model was then applied to predict the DDI effect with other CYP3A4 inhibitors or inducers and the DDI potential with midazolam under steady-state conditions. The simulated midazolam area under the curve ratio of 0.54 and an accompanying observed 1.9-fold increase in the CYP3A4 activity of biomarker 4ß-hydroxycholesterol indicated a weak-to-moderate CYP3A4 induction by midostaurin and its metabolites at steady state in patients with advSM. In conclusion, a simultaneous parent-and-active-metabolite modeling approach allowed predictions under steady-state conditions that were not possible to achieve in healthy subjects. Furthermore, endogenous biomarker data enabled evaluation of the net effect of midostaurin and its metabolites on CYP3A4 activity at steady state and increased confidence in DDI predictions.

A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series.

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

Estimation of fractions metabolized by hepatic CYP enzymes using a concept of inter-system extrapolation factors (ISEFs) - a comparison with the chemical inhibition method.

BACKGROUND: For estimation of fractions metabolized (fm) by different hepatic recombinant human CYP enzymes (rhCYP), calculation of inter-system extrapolation factors (ISEFs) has been proposed. METHODS: ISEF values for CYP1A2, CYP2C19 and CYP3A4/5 were measured. A CYP2C9 ISEF was taken from a previous report. Using a set of compounds, fractions metabolized by CYP enzymes (fm,CYP) values calculated with the ISEFs based on rhCYP data were compared with those from the chemical inhibition data. Oral pharmacokinetics (PK) profiles of midazolam were simulated using the physiologically based pharmacokinetics (PBPK) model with the CYP3A ISEF. For other CYPs, the in vitro fm,CYP values were compared with the reference fm,CYP data back-calculated with, e.g. modeling of test substrates by feeding clinical PK data. RESULTS: In vitro-in vitro fm,CYP3A4 relationship between the results from rhCYP incubation and chemical inhibition was drawn as an exponential correlation with R2=0.974. A midazolam PBPK model with the CYP3A4/5 ISEFs simulated the PK profiles within twofold error compared to the clinical observations. In a limited number of cases, the in vitro methods could not show good performance in predicting fm,CYP1A2, fm,CYP2C9 and fm,CYP2C19 values as reference data. CONCLUSIONS: The rhCYP data with the measured ISEFs provided reasonable calculation of fm,CYP3A4 values, showing slight over-estimation compared to chemical inhibition.

Physiologically Based Pharmacokinetic Model Predictions of Panobinostat (LBH589) as a Victim and Perpetrator of Drug-Drug Interactions.

Panobinostat (Farydak) is an orally active hydroxamic acid-derived histone deacetylase inhibitor used for the treatment of relapsed or refractory multiple myeloma. Based on recombinant cytochrome P450 (P450) kinetic analyses in vitro, panobinostat oxidative metabolism in human liver microsomes was mediated primarily by CYP3A4 with lower contributions by CYP2D6 and CYP2C19. Panobinostat was also an in vitro reversible and time-dependent inhibitor of CYP3A4/5 and a reversible inhibitor of CYP2D6 and CYP2C19. Based on a previous clinical drug-drug interaction study with ketoconazole (KTZ), the contribution of CYP3A4 in vivo was estimated to be ∼40%. Using clinical pharmacokinetic (PK) data from several trials, including the KTZ drug-drug interaction (DDI) study, a physiologically based pharmacokinetic (PBPK) model was built to predict panobinostat PK after single and multiple doses (within 2-fold of observed values for most trials) and the clinical DDI with KTZ (predicted and observed area under the curve ratios of 1.8). The model was then applied to predict the drug interaction with the strong CYP3A4 inducer rifampin (RIF) and the sensitive CYP3A4 substrate midazolam (MDZ) in lieu of clinical trials. Panobinostat exposure was predicted to decrease in the presence of RIF (65%) and inconsequentially increase MDZ exposure (4%). Additionally, PBPK modeling was used to examine the effects of stomach pH on the absorption of panobinostat in humans and determined that absorption of panobinostat is not expected to be affected by increases in stomach pH. The results from these studies were incorporated into the Food and Drug Administration-approved product label, providing guidance for panobinostat dosing recommendations when it is combined with other drugs.

Integrated TK-TD modeling for drug-induced concurrent tachycardia and QT changes in beagle dogs.

Drug-induced cardiotoxicity, including tachycardia and QT prolongation, remains a major safety concern that needs to be identified and its risk mitigated in early stages of drug development. In the present study, an integrated toxicokinetic-toxicodynamic (TK-TD) modeling approach within a nonlinear mixed-effect modeling framework is applied to investigate concurrent abnormal heart rate and QT changes in three beagle dogs, using a Novartis internal compound (NVS001) as the case example. By accounting for saturable drug absorption, circadian rhythms, drug-effect tolerance, and nonlinear rate-dependency of QT interval, the dynamic TK-TD model captures the experimentally observed drug effects on heart rate and QT interval across a wide dosing range of NVS001 in beagle dogs. Further analyses reveal that the NVS001-induced QT prolongation observed in the low-dose groups is potentially caused by direct drug inhibition on the hERG channel, while the apparent QT shortening in the high-dose groups may be due to strong rate-dependency of QT at high heart rates. This study also suggests that the TK-TD model can be used to identify direct drug effects on the non-rate-dependent QT component by dissociating QT changes from tachycardia and deriving a new QT correction method. The integrated TK-TD model presented here may serve as a novel quantitative framework for evaluating drug-induced concurrent changes in heart rate and QT to potentially facilitate preclinical and clinical safety studies.

Midostaurin, a Novel Protein Kinase Inhibitor for the Treatment of Acute Myelogenous Leukemia: Insights from Human Absorption, Metabolism, and Excretion Studies of a BDDCS II Drug.

The absorption, metabolism, and excretion of midostaurin, a potent class III tyrosine protein kinase inhibitor for acute myelogenous leukemia, were evaluated in healthy subjects. A microemulsion formulation was chosen to optimize absorption. After a 50-mg [14C]midostaurin dose, oral absorption was high (>90%) and relatively rapid. In plasma, the major circulating components were midostaurin (22%), CGP52421 (32.7%), and CGP62221 (27.7%). Long plasma half-lives were observed for midostaurin (20.3 hours), CGP52421 (495 hours), and CGP62221 (33.4 hours). Through careful mass-balance study design, the recovery achieved was good (81.6%), despite the long radioactivity half-lives. Most of the radioactive dose was recovered in feces (77.6%) mainly as metabolites, because only 3.43% was unchanged, suggesting mainly hepatic metabolism. Renal elimination was minor (4%). Midostaurin metabolism pathways involved hydroxylation, O-demethylation, amide hydrolysis, and N-demethylation. High plasma CGP52421 and CGP62221 exposures in humans, along with relatively potent cell-based IC50 for FMS-like tyrosine kinase 3-internal tandem duplications inhibition, suggested that the antileukemic activity in AML patients may also be maintained by the metabolites. Very high plasma protein binding (>99%) required equilibrium gel filtration to identify differences between humans and animals. Because midostaurin, CGP52421, and CGP62221 are metabolized mainly by CYP3A4 and are inhibitors/inducers for CYP3A, potential drug-drug interactions with mainly CYP3A4 modulators/CYP3A substrates could be expected. Given its low aqueous solubility, high oral absorption and extensive metabolism (>90%), midostaurin is a Biopharmaceutics Classification System/Biopharmaceutics Drug Disposition Classification System (BDDCS) class II drug in human, consistent with rat BDDCS in vivo data showing high absorption (>90%) and extensive metabolism (>90%).

Evaluation of Drug-Drug Interaction Potential Between Sacubitril/Valsartan (LCZ696) and Statins Using a Physiologically Based Pharmacokinetic Model.

Sacubitril/valsartan (LCZ696) has been approved for the treatment of heart failure. Sacubitril is an in vitro inhibitor of organic anion-transporting polypeptides (OATPs). In clinical studies, LCZ696 increased atorvastatin Cmax by 1.7-fold and area under the plasma concentration-time curve by 1.3-fold, but had little or no effect on simvastatin or simvastatin acid exposure. A physiologically based pharmacokinetics modeling approach was applied to explore the underlying mechanisms behind the statin-specific LCZ696 drug interaction observations. The model incorporated OATP-mediated clearance (CLint,T) for simvastatin and simvastatin acid to successfully describe the pharmacokinetic profiles of either analyte in the absence or presence of LCZ696. Moreover, the model successfully described the clinically observed drug effect with atorvastatin. The simulations clarified the critical parameters responsible for the observation of a low, yet clinically relevant, drug-drug interaction DDI between sacubitril and atorvastatin and the lack of effect with simvastatin acid. Atorvastatin is administered in its active form and rapidly achieves Cmax that coincide with the low Cmax of sacubitril. In contrast, simvastatin requires a hydrolysis step to the acid form and therefore is not present at the site of interactions at sacubitril concentrations that are inhibitory. Similar models were used to evaluate the drug-drug interaction risk for additional OATP-transported statins which predicted to maximally result in a 1.5-fold exposure increase.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 3: Identifying gaps in system parameters by analysing In Silico performance across different compound classes.

Three Physiologically Based Pharmacokinetic software packages (GI-Sim, Simcyp® Simulator, and GastroPlus™) were evaluated as part of the Innovative Medicine Initiative Oral Biopharmaceutics Tools project (OrBiTo) during a blinded "bottom-up" anticipation of human pharmacokinetics. After data analysis of the predicted vs. measured pharmacokinetics parameters, it was found that oral bioavailability (Foral) was underpredicted for compounds with low permeability, suggesting improper estimates of intestinal surface area, colonic absorption and/or lack of intestinal transporter information. Foral was also underpredicted for acidic compounds, suggesting overestimation of impact of ionisation on permeation, lack of information on intestinal transporters, or underestimation of solubilisation of weak acids due to less than optimal intestinal model pH settings or underestimation of bile micelle contribution. Foral was overpredicted for weak bases, suggesting inadequate models for precipitation or lack of in vitro precipitation information to build informed models. Relative bioavailability was underpredicted for both high logP compounds as well as poorly water-soluble compounds, suggesting inadequate models for solubility/dissolution, underperforming bile enhancement models and/or lack of biorelevant solubility measurements. These results indicate areas for improvement in model software, modelling approaches, and generation of applicable input data. However, caution is required when interpreting the impact of drug-specific properties in this exercise, as the availability of input parameters was heterogeneous and highly variable, and the modellers generally used the data "as is" in this blinded bottom-up prediction approach.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 2: An introduction to the simulation exercise and overview of results.

Orally administered drugs are subject to a number of barriers impacting bioavailability (Foral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters. Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details. 43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.). Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, Foral and relative AUC (Frel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations. A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.