Physiologically based pharmacokinetic modelling to predict single- and multiple-dose human pharmacokinetics of bitopertin

Phase I clinical trial of NH130 and the prediction of its pharmacokinetics using physiologically based pharmacokinetic modeling

Background

Parkinson's disease psychosis (PDP) is a common and distressing complication of Parkinson's disease (PD), characterized by hallucinations and delusions. This research aimed to assess the pharmacokinetics and safety of NH130, a selective serotonin 5-HT2A inverse agonist, as a potential PDP treatment in healthy individuals.

Methods

We conducted clinical pharmacokinetic studies and safety evaluations for NH130, employing a physiologically based pharmacokinetic (PBPK) model to predict its behavior in human body.

Results

In a single-dose escalation study, healthy volunteers received NH130 at varying doses (2 mg, 6 mg, 12 mg, 24 mg, 40 mg, 60 mg, and 90 mg) or a placebo. The drug demonstrated favorable pharmacokinetics, with no serious adverse events (AEs) reported. Clinical plasma concentrations correlated well with PBPK model predictions, validating the model's utility for guiding future clinical development.

Conclusion

NH130 showed promising pharmacokinetic characteristics and safety profile, supporting its progression to multi-dose trials and suggesting its potential as a therapeutic agent for PDP.

Clinical Trial Registration

http://www.chinadrugtrials.org.cn/index.html, Identifier CTR20230409.

Role of Physiologically Based Biopharmaceutics Modeling (PBBM) in Fed Bioequivalence Study Waivers: Regulatory Outlook, Case Studies and Future Perspectives

Over the past few decades, physiologically based biopharmaceutics modeling (PBBM) has demonstrated its utility in both new drug and generic product development. Applications of PBBM for fed bioequivalence study waivers is an upcoming area. Recently Innovation & Quality (IQ) consortium demonstrated utility of PBBM to avoid repeat food effect studies for new drugs. In the similar lines, the current manuscript aims to discuss role of PBBM in generic fed bioequivalence study waivers. Generic industry practices related to PBBM model development to predict fed bioequivalence was portrayed with special emphasis on fed bio-predictive media. Media that can simulate fed bioequivalence study outcome were discussed from practical perspective. In-depth analysis, collating the data from 36 products was performed to understand predictability of PBBM for fed bioequivalence. Cases where PBBM was successful to predict fed bioequivalence was correlated with BCS class, formulation category and type of food effect. Further, two case studies were presented wherein fed bioequivalence study waiver obtained with PBBM approach. Lastly, future direction in terms of fed bioequivalence study waivers, regulatory perspectives and best practices for PBBM were portrayed. Overall, this article paves a way to utilize PBBM for generic fed bioequivalence study waivers.

Pharmacokinetic profile of bitopertin, a selective GlyT1 inhibitor, in the rat

Bitopertin, a selective glycine transporter 1 (GlyT₁) inhibitor, has been extensively studied for the treatment of schizophrenia, with known safety and tolerability profiles in the clinic. Whereas several rodent experiments have been reported, the pharmacokinetic (PK) profile of bitopertin in rodents has not been extensively reported, as only two studies disclosed limited PK parameters in male rats after oral administration. Here, we determined the PK profile of bitopertin in female Sprague-Dawley rats. Blood samples were taken serially, before and after sub-cutaneous (0.03, 0.1, 0.3, 1, and 3 mg/kg) or intra-venous (0.1 mg/kg) administration. Plasma levels were determined by high-performance liquid chromatography coupled with heat-assisted electrospray ionisation tandem mass spectrometry (HPLC-HESI MS/MS). Subsequently, PK parameters were calculated using non-compartmental analysis, including area under the curve (AUC), time (T_max) to maximal plasma concentration (C_max), clearance (CL), volume of distribution (V_z), as well as half-life (T_1/2). Following sub-cutaneous injection, bitopertin exhibited dose-dependent AUC_0-∞ (439.6-34,018.9 ng/mL) and T_max (3.7-24.0 h), a very long terminal T_1/2 (35.06-110.32 h) and low CL (0.07-0.13 L/h/kg), suggesting that bitopertin is slowly absorbed and eliminated in the rat. The observed relationship between dose and the extent of drug exposure (AUC) was linear. Following administration of all sub-cutaneous doses, measured bitopertin plasma levels were comparable to levels achieved with doses already administered in the clinic. We hope that our results will be useful in the design of pre-clinical experiments in which this drug will eventually be administered sub-cutaneously.

First-in-human study to investigate the safety and pharmacokinetics of salvianolic acid A and pharmacokinetic simulation using a physiologically based pharmacokinetic model

Salvianolic acid A (SAA) is a water-soluble phenolic acid component from Salvia miltiorrhiza Bunge currently under development for myocardial protection treatment for coronary heart disease (CHD). We investigated the safety, tolerability, and pharmacokinetics of single and multiple ascending doses of SAA. Additionally, a physiologically based pharmacokinetic (PBPK) model was developed to simulate the pharmacokinetics of SAA. This was a first-in-human (FIH), randomized, double-blind, placebo-controlled, single, and multiple-dose study in 116 healthy Chinese subjects with the range of 10-300 mg and 60-200 mg SAA, respectively. SAA was well tolerated at all dose levels, following both single and multiple doses, with a low overall incidence of treatment-emergent adverse events (TEAEs) which appeared to be no dose-related. The main pharmacokinetic parameter of SAA, assessed by the power model, was the lack of proportionality with the dose range after single dosing. The 90% CIs of the slope β of Cmax (1.214 [1.150-1.278]) and AUC0-t (1.222 [1.156-1.288]) were not within the predefined acceptance range, and the direction of the deviation was higher than expected. PBPK modeling suggested the transfer ability saturation of hepatic organic anion-transporting polypeptide 1B1 (OATP1B1) and P-glycoprotein (P-gp) might result in a relatively low distribution rate at higher doses. Clinical plasma concentrations observed were in good agreement with PBPK prediction. SAA showed well-characterized pharmacokinetics and was generally well tolerated in the dose range investigated. The PBPK model provides valuable pharmacokinetic knowledge for further clinical development.

Effect of glycine transporter 1 inhibition with bitopertin on parkinsonism and L-DOPA induced dyskinesia in the 6-OHDA-lesioned rat

Dyskinesia remains an unmet need in Parkinson's disease (PD). We have previously demonstrated that glycine transporter 1 (GlyT1) inhibition with ALX-5407 reduces dyskinesia and slightly improves parkinsonism in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned marmoset. Here, we sought to determine the effect of bitopertin, a clinically-tested GlyT1 inhibitor, on parkinsonism and dyskinesia in the 6-hydroxydopamine (6-OHDA)-lesioned rat. To do so, we assessed the effect of bitopertin on parkinsonism as monotherapy and as adjunct to a low dose of L-3,4-dihydroxyphenylalanine (L-DOPA). We then assessed the efficacy of bitopertin on dyskinesia in the context of acute challenge and chronic administration studies. Lastly, we evaluated whether de novo treatment with bitopertin, started concurrently with L-DOPA, would diminish the development of dyskinesia. We discovered that bitopertin (0.3 mg/kg), when administered alone, reduced the severity of parkinsonism by 35% (P < 0.01). As adjunct to a low dose of L-DOPA, bitopertin (3 mg/kg) enhanced the anti-parkinsonian effect of L-DOPA by 36% (P < 0.05). Moreover, the acute addition of bitopertin (0.03 mg/kg) to L-DOPA reduced dyskinesia by 27% (P < 0.001), and there was no tolerance to the anti-dyskinetic benefit after 4 weeks of daily administration. Lastly, bitopertin (0.03 mg/kg) started concurrently with L-DOPA, also attenuated the development of dyskinesia, by 33% (P < 0.01), when compared to L-DOPA alone. Our results suggest that GlyT1 inhibition may simultaneously reduce parkinsonism and L-DOPA-induced dyskinesia and represents a novel approach to treat, possibly prevent, motor complications in PD.

Introduction of an artificial neural network–based method for concentration‐time predictions

Pharmacometrics and the application of population pharmacokinetic (PK) modeling play a crucial role in clinical pharmacology. These methods, which describe data with well‐defined equations and estimate physiologically interpretable parameters, have not changed substantially during the past decades. Although the methods have proven their usefulness, they are often resource intensive and require a high level of expertise. We investigated whether a method based on artificial neural networks (ANNs) may provide an alternative approach for the prediction of concentration‐time curve to supplement the gold standard methods. In this work, we used simulated data to overcome the requirement for a large clinical training data set, implemented a pharmacologically reasonable network architecture to improve extrapolation to different dosing schemes, and used transfer learning to quickly adapt the predictions to new patient groups. We demonstrate that ANNs are able to learn the shape of concentration‐time curves and make individual predictions based on a short sequence of PK measurements. Furthermore, an ANN trained on simulated data was applied to real clinical data and was demonstrated to extrapolate to different dosing schemes. We also adapted the ANN trained on simulated healthy subjects to simulated hepatic impaired patients through transfer learning. In summary, we demonstrate how ANNs could be leveraged in a PK workflow to efficiently make individual concentration‐time predictions, and we discuss the current limitations and advantages of such an ANN‐based method.

Food Effect Projections via Physiologically Based Pharmacokinetic Modeling: Predictive Case Studies

Food can alter the absorption of orally administered drugs. Biopharmaceutics physiologically based pharmacokinetic (PBPK) modeling offers the possibility to simulate a compound's pharmacokinetics under fasted or fed states. To advance the utility of PBPK modeling, with a view to regulatory impact, we have pooled our experience across 4 pharmaceutical companies to propose a general multistep PBPK workflow leveraging pre-existing clinical data for immediate-release formulations of Biopharmaceutics Classification System I and II compounds. With this strategy, we wish to promote pragmatic PBPK approaches for compounds where absorption is well understood, that is, compounds with moderate-to-high permeability that are not substrates for uptake transporters. Five case studies demonstrate how food effect can be well predicted using appropriately established and validated models. The case studies integrate solubility and dissolution data for initial model development and apply a "middle-out" validation with clinical data in one prandial state. Then, whenever possible, a validation against both fasted and fed state data is recommended before application of the models prospectively for to-be-marketed formulations. Thus, when combined with limited clinical data, PBPK models could be used to simulate outcomes for new doses, formulations, or active pharmaceutical ingredient forms, in lieu of a clinical food-effect study.

Combining 'Bottom-Up' and 'Top-Down' Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example

Background and Objectives

We propose a strategy for studying ethnopharmacology by conducting sequential physiologically based pharmacokinetic (PBPK) prediction (a ‘bottom-up’ approach) and population pharmacokinetic (popPK) confirmation (a ‘top-down’ approach), or in reverse order, depending on whether the purpose is ethnic effect assessment for a new molecular entity under development or a tool for ethnic sensitivity prediction for a given pathway. The strategy is exemplified with bitopertin.

Methods

A PBPK model was built using Simcyp^® to simulate the pharmacokinetics of bitopertin and to predict the ethnic sensitivity in clearance, given pharmacokinetic data in just one ethnicity. Subsequently, a popPK model was built using NONMEM^® to assess the effect of ethnicity on clearance, using human data from multiple ethnic groups. A comparison was made to confirm the PBPK-based ethnic sensitivity prediction, using the results of the popPK analysis.

Results

PBPK modelling predicted that the bitopertin geometric mean clearance values after 20 mg oral administration in Caucasians would be 1.32-fold and 1.27-fold higher than the values in Chinese and Japanese, respectively. The ratios of typical clearance in Caucasians to the values in Chinese and Japanese estimated by popPK analysis were 1.20 and 1.17, respectively. The popPK analysis results were similar to the PBPK modelling results.

Conclusion

As a general framework, we propose that PBPK modelling should be considered to predict ethnic sensitivity of pharmacokinetics prior to any human data and/or with data in only one ethnicity. In some cases, this will be sufficient to guide initial dose selection in different ethnicities. After clinical trials in different ethnicities, popPK analysis can be used to confirm ethnic differences and to support dose justification and labelling. PBPK modelling prediction and popPK analysis confirmation can complement each other to assess ethnic differences in pharmacokinetics at different drug development stages.

Electronic supplementary material

The online version of this article (doi:10.1007/s40262-015-0356-1) contains supplementary material, which is available to authorized users.

Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities

PURPOSE OF REVIEW

This review gives a perspective on the current "state of the art" in metabolic drug-drug interaction (DDI) prediction. We highlight areas of successful prediction and illustrate progress in areas where limits in scientific knowledge or technologies prevent us from having full confidence.

RECENT FINDINGS

Several examples of success are highlighted. Work done for bitopertin shows how in vitro and clinical data can be integrated to give a model-based understanding of pharmacokinetics and drug interactions. The use of interpolative predictions to derive explicit dosage recommendations for untested DDIs is discussed using the example of ibrutinib, and the use of DDI predictions in lieu of clinical studies in new drug application packages is exemplified with eliglustat and alectinib. Alectinib is also an interesting case where dose adjustment is unnecessary as the activity of a major metabolite compensates sufficiently for changes in parent drug exposure. Examples where "unusual" cytochrome P450 (CYP) and non-CYP enzymes are responsible for metabolic clearance have shown the importance of continuing to develop our repertoire of in vitro regents and techniques. The time-dependent inhibition assay using human hepatocytes suspended in full plasma allowed improved DDI predictions, illustrating the importance of continued in vitro assay development and refinement.

SUMMARY

During the past 10 years, a highly mechanistic understanding has been developed in the area of CYP-mediated metabolic DDIs enabling the prediction of clinical outcome based on preclinical studies. The combination of good quality in vitro data and physiologically based pharmacokinetic modeling may now be used to evaluate DDI risk prospectively and are increasingly accepted in lieu of dedicated clinical studies.

Effects of Cytochrome P450 3A4 Inhibitors-Ketoconazole and Erythromycin-on Bitopertin Pharmacokinetics and Comparison with Physiologically Based Modelling Predictions

OBJECTIVE

To assess the effect of strong and moderate cytochrome P450 (CYP) 3A4 inhibition on exposure of bitopertin, a glycine reuptake inhibitor primarily metabolized by CYP3A4, and to compare the results with predictions based on physiologically based pharmacokinetic (PBPK) modelling.

METHODS

The effects of ketoconazole and erythromycin were assessed in two male volunteer studies with open-label, two-period, fixed-sequence designs. Twelve subjects were enrolled in each of the studies. In period 1, a single dose of bitopertin was administered; in period 2, 400 mg ketoconazole was administered once daily for 17 days or 500 mg erythromycin was administered twice daily for 21 days. A single dose of bitopertin was coadministered on day 5. Pharmacokinetic parameters were derived by non-compartmental methods. Simulated bitopertin profiles using dynamic PBPK modelling for a typical healthy volunteer in GastroPlus(®) were used to predict changes in pharmacokinetic parameters.

RESULTS

In healthy volunteers, coadministration of ketoconazole increased the bitopertin area under the plasma concentration-time curve (AUC) from 0 to 312 h (AUC0-312h) 4.2-fold (90 % confidence interval [CI] 3.5-5.0) and erythromycin increased the AUC from time zero to infinity (AUC0-inf) 2.1-fold (90 % CI 1.9-2.3). The peak concentration (C max) increased by <25 % in both studies. Simulated bitopertin profiles using PBPK modelling showed good agreement with the observed AUC ratios in both studies. The predicted AUC0-inf ratios for the interaction with ketoconazole and erythromycin were 7.7 and 1.9, respectively.

CONCLUSION

Strong CYP3A4 inhibitors increase AUC0-inf of bitopertin 7- to 8-fold and hence should not be administered concomitantly with bitopertin. Moderate CYP3A4 inhibitors double AUC0-inf.

Effects of the glycine reuptake inhibitors bitopertin and RG7118 on glycine in cerebrospinal fluid: results of two proofs of mechanism studies in healthy volunteers

RATIONALE

Hypofunction of NMDA receptors has been implicated in neuropsychiatric disorders including schizophrenia. NMDA receptor neurotransmission can be enhanced through inhibition of glycine reuptake by the glycine transporter type 1 (GlyT1).

OBJECTIVES

The primary objective of these studies was to explore the relationship between plasma exposure and glycine cerebrospinal fluid (CSF) concentrations following administration of bitopertin and RG7118 in healthy volunteers.

METHODS

The bitopertin study comprised four dose levels (3, 10, 30 and 60 mg) administered once daily for 10 days. In the RG7118 study, placebo, 15 or 30 mg RG7118 was administered once daily for 28 days. CSF samples were taken on day -2 and day 10, and day -1 and day 26 for bitopertin and RG7118, respectively.

RESULTS

Twenty-two and 24 subjects participated in the bitopertin and RG7118 study, respectively. In the bitopertin study, CSF glycine concentrations showed a dose-dependent increase from baseline to day 10. The geometric mean ratios (coefficient of variation) of AUC0-12 h on day 10 over baseline were 1.3 (17 %), 1.3 (49 %), 1.7 (18 %) and 2.3 (14 %) after 3, 10, 30 and 60 mg, respectively. In the RG7118 study, the geometric mean ratio of glycine concentration (CV) on day 26 at 6 h post-dose over time-matched baseline was approx. 1.9 (24 and 15 %) for 15 and 30 mg.

CONCLUSIONS

The mechanism of action of bitopertin and RG7118, i.e. inhibition of glycine reuptake in the brain, was confirmed. The maximal increase observed in healthy volunteers was similar to the one observed in animals showing the good translatability of this biomarker.

Physiologically based absorption modelling to predict the impact of drug properties on pharmacokinetics of bitopertin

Bitopertin (RG1678) is a glycine reuptake inhibitor in phase 3 trials for treatment of schizophrenia. Its clinical oral pharmacokinetics is sensitive to changes in drug substance particle size and dosage form. Physiologically based pharmacokinetic (PBPK) absorption model simulations of the impact of changes in particle size and dosage form (either capsules, tablets, or an aqueous suspension) on oral pharmacokinetics was verified by comparison to measured plasma concentrations. Then, a model parameter sensitivity analysis was applied to set limits on the particle sizes included in tablets for the market. The model was also used to explore the in vitro to in vivo correlation. Simulated changes in oral pharmacokinetics caused by differences in particle size and dosage form were confirmed in two separate relative bioavailability studies. Model parameter sensitivity analyses predicted that AUCinf was hardly reduced as long as particle diameter (D50) remained smaller than 30 μm, and >20% reduced Cmax is anticipated only when particle diameter exceeds 15 μm. An exploration of the sensitivity to the presence of larger particles within a polydisperse distribution showed that simulated Cmax is again more affected than AUC but is less than 20% reduced as long as D50 is less than 8 μm and D90 is smaller than 56 μm. PBPK absorption modelling can contribute to a quality by design (QbD) approach for clinical formulation development and support the setting of biorelevant specifications for release of the product.