Toward an integrated human clearance prediction strategy that minimizes animal use

Cellulose ether and carbopol 971 based gastroretentive controlled release formulation design, optimization and physiologically based pharmacokinetic modeling of ondansetron hydrochloride minitablets

This study aims to design and optimize ondansetron (OND) gastro-retentive floating minitablets for better and prolonged control of postoperative nausea and vomiting (PONV) with improved patient compliance. Minitablets were directly compressed and encapsulated in a size 2 capsule shell with an overall dose of 24 mg. Central composite design (CCD) was applied keeping one cellulose ether derivative HPMC K15M and Carbopol 971 as variable and used as swelling and rate retarding agents. The other cellulose derivative i.e. sodium carboxymethyl cellulose, along with mannitol, sodium bicarbonate, and talc, were used in fixed quantities. The floating lag time, total floating time, swelling index, in-vitro drug release, and zero-order (RSQ value), were critical quality parameters. The optimized formulation (Fpred) was evaluated for all critical parameters, along with surface morphology, thermal stability, chemical interaction, and accelerated stability. The in silico PBPK modeling was applied to compare the bioavailability of Fpred with reference OND immediate-release tablets. The numerical optimization model predicted >90 % drug release with zero-order at 12 h. In silico PBPK modeling revealed comparable relative bioavailability of Fpred with the reference formulation. The gastroretentive floating minitablets of OND were successfully designed for prolonged emesis control in patients receiving chemotherapeutic agents.

Ulgen K. Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials

Nanoparticles (NPs) have been widely used to improve the pharmacokinetic properties and tissue distribution of small molecules such as targeting to a specific tissue of interest, enhancing their systemic circulation, and enlarging their therapeutic properties. NPs have unique and complicated in vivo disposition properties compared to small molecule drugs due to their complex multifunctionality. Physiologically based pharmacokinetic (PBPK) modeling has been a powerful tool in the simulation of the absorption, distribution, metabolism, and elimination (ADME) characteristics of the materials, and it can be used in the characterization and prediction of the systemic disposition, toxicity, efficacy, and target exposure of various types of nanoparticles. In this review, recent advances in PBPK model applications related to the nanoparticles with unique properties, and dispositional features in the biological systems, ADME characteristics, the description of transport processes of nanoparticles in the PBPK model, and the challenges in PBPK model development of nanoparticles are delineated and juxtaposed with those encountered in small molecule models. Nanoparticle related, non-nanoparticle-related, and interspecies-scaling methods applied in PBPK modeling are reviewed. In vitro to in vivo extrapolation (IVIVE) methods being a promising computational tool to provide in vivo predictions from the results of in vitro and in silico studies are discussed. Finally, as a recent advancement ML/AI-based approaches and challenges in PBPK modeling in the estimation of ADME parameters and pharmacokinetic (PK) analysis results are introduced.

Palladium/GF-Phos-catalyzed asymmetric carbenylative amination to access chiral pyrrolidines and piperidines

The cross-coupling of N-tosylhydrazones has emerged as a powerful method for the construction of structurally diverse molecules, but the development of catalytic enantioselective versions still poses considerable challenges and only very limited examples have been reported. We herein report an asymmetric palladium/GF-Phos-catalyzed carbenylative amination reaction of N-tosylhydrazones and (E)-vinyl iodides pendent with amine, which allows facile access to a range of chiral pyrrolidines and piperidines in good yields (45-93%) with up to 96.5 : 3.5 er. Moreover, mild conditions, general substrate scope, scaled-up preparation, as well as the efficient synthesis of natural product (-)-norruspoline are practical features of this method.

Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models

Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fu_mic) from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (r²=0.86) or mouse (r²=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes (r² of >0.89, n = 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human fu_mic using four different in silico models for a set of 53-60 compounds. The correlation (r²) and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r²=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r²<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.

Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator

The Simcyp Simulator is a software platform for population physiologically‐based pharmacokinetic (PBPK) modeling and simulation. It links in vitro data to in vivo absorption, distribution, metabolism, excretion and pharmacokinetic/pharmacodynamic outcomes to explore clinical scenarios and support drug development decisions, including regulatory submissions and drug labels. This tutorial describes the different input parameters required, as well as the considerations needed when developing a PBPK model within the Simulator, for a small molecule intended for oral administration. A case study showing the development and application of a PBPK model for ondansetron is herein used to aid the understanding of different PBPK model development concepts.

Preterm Physiologically Based Pharmacokinetic Model. Part II: Applications of the Model to Predict Drug Pharmacokinetics in the Preterm Population

BACKGROUND

Preterm neonates are usually not part of a traditional drug development programme, however they are frequently administered medicines. Developing modelling and simulation tools, such as physiologically based pharmacokinetic (PBPK) models that incorporate developmental physiology and maturation of drug metabolism, can be used to predict drug exposure in this group of patients, and may help to optimize drug dose adjustment.

OBJECTIVE

The aim of this study was to assess and verify the predictability of a preterm PBPK model using compounds that undergo diverse renal and/or hepatic clearance based on the knowledge of their disposition in adults.

METHODS

A PBPK model was developed in the Simcyp Simulator V17 to predict the pharmacokinetics (PK) of drugs in preterm neonates. Drug parameters for alfentanil, midazolam, caffeine, ibuprofen, gentamicin and vancomycin were collated from the literature. Predicted PK parameters and profiles were compared against the observed data.

RESULTS

The preterm PBPK model predicted the PK changes of the six compounds using ontogeny functions for cytochrome P450 (CYP) 1A2, CYP2C9 and CYP3A4 after oral and intravenous administrations. For gentamicin and vancomycin, the maturation of renal function was able to predict the exposure of these two compounds after intravenous administration. All PK parameter predictions were within a twofold error criteria.

CONCLUSION

While the developed preterm model for the prediction of PK behaviour in preterm patients is not intended to replace clinical studies, it can potentially help with deciding on first-time dosing in this population and study design in the absence of clinical data.

In vitro-in vivo extrapolation of metabolic clearance using human liver microsomes: factors showing variability and their normalization

In vitro-in vivo extrapolation (IVIVE) using human liver microsomes has been widely used to predict metabolic clearance, but some of the factors used in the process of prediction show variability for the same compound: notably, microsomal intrinsic clearance values corrected by the unbound fraction (CL_{int, u}), physiological parameters used for scale-up, and the source of in vivo clearance data.The purpose of this study was to assess the correlation between in vitro and in vivo CL_int with a focus on factors showing variability using four cytochrome P450 (CYP)3A substrates.We surveyed in vivo clearance values in literature and also determined the microsomal CL_{int, u} values. A scaling factor (SF_direct) was defined as in vivo CL_int divided by the microsomal CL_{int, u}, which ranged from 1190 to 2310 (mg protein per kg body weight). The application of a mean SF_direct of 1600 (mg protein per kg body weight) and further normalization by the microsomal CL_{int, u} values of midazolam, the most commonly used substrate, resulted in improved prediction accuracy for CL_{int, u} values from various microsomal batches.The results suggest the normalization of variability might be useful for predicting the in vivo CL_int.

Comparative pharmaceutical metabolism by rainbow trout (Oncorhynchus mykiss) liver S9 fractions

The occurrence of pharmaceuticals in the environment presents a challenge of growing concern. In contrast to many industrial compounds, pharmaceuticals undergo extensive testing prior to their introduction to the environment. In principle, therefore, it may be possible to employ existing pharmacological safety data using biological "read-across" methods to support screening-level bioaccumulation environmental risk assessment. However, few approaches and robust empirical data sets exist, particularly for comparative pharmacokinetic applications. For many pharmaceuticals, the primary cytochrome P450 (CYP) enzymes responsible for their metabolism have been identified in humans. The purpose of the present study was to employ a comparative approach to determine whether rainbow trout biotransform pharmaceuticals known to be substrates for specific human CYPs. Seven compounds were selected based on their primary metabolism in humans by CYP3A4, CYP2D6, or CYP2C9. Five additional test compounds are known to be substrates for multiple CYPs. Metabolism by rainbow trout liver S9 fractions was evaluated using a substrate-depletion approach, which provided an estimate of intrinsic hepatic clearance (CLIN VITRO,INT ). An isotope dilution liquid chromatography-tandem mass spectrometry method was employed for quantitation of parent chemical concentrations. Only 2 general CYP substrates demonstrated measurable levels of substrate depletion. No significant biotransformation was observed for known substrates of human CYP2D6, CYP2C9, or CYP3A4. The results of this study provide novel information for therapeutics that fish models are likely to metabolize based on existing mammalian data. Further, these results suggest that pharmaceuticals may possess a greater tendency to bioaccumulate in fish than previously anticipated.

Dogs and monkeys in preclinical drug development: the challenge of reducing and replacing

INTRODUCTION

Animal experimentation is a very contentious issue affecting reputation of drug industry. There are several reasons to forecast an increase in the number of dogs and monkeys used in safety and pharmacokinetic studies. This increase may trigger a strong reaction of the public opinion. There have been many proposals and initiatives to change the present approach to safety and metabolic studies. Tests based on new technologies, in vitro cell assays, stem cells, imaging, and computational systems, have the potential to anticipate effects in humans. Unfortunately, all these efforts and ideas have not changed standard approaches and regulatory expectations.

AREAS COVERED

This review looks at opportunities to reduce the number of dogs and monkeys currently used in pharmaceutical research. It also discusses present efforts and approaches, their strengths and potentials and the reasons why they may not fulfill expectations.

EXPERT OPINION

Unless the pharmaceutical industry gets more involved, an alternative paradigm of preclinical drug development is unlikely to be established in the foreseeable future. One can imagine a scenario where the political pressure against the use of dogs and monkeys in biomedical research becomes irresistible while alternative methods are not yet established. To avoid this situation, the pharmaceutical industry should take the lead and preclinical scientists at all levels need to influence decision makers and help develop new innovative approaches in drug safety evaluation.

Comparative assessment of In Vitro-In Vivo extrapolation methods used for predicting hepatic metabolic clearance of drugs

The purpose of this study was to perform a comparative analysis of various in vitro--in vivo extrapolation (IVIVE) methods used for predicting hepatic metabolic clearance (CL) of drugs on the basis of intrinsic CL data determined in microsomes. Five IVIVE methods were evaluated: the "conventional and conventional bias-corrected methods" using the unbound fraction in plasma (fu(p) ), the "Berezhkovskiy method" in which the fu(p) is adjusted for drug ionization, the "Poulin et al. method" using the unbound fraction in liver (fu(liver) ), and the "direct scaling method," which does not consider any binding corrections. We investigated the effects of the following scenarios on the prediction of CL: the use of preclinical or human datasets, the extent of plasma protein binding, the magnitude of CL in vivo, and the extent of drug disposition based on biopharmaceutics drug disposition classification system (BDDCS) categorization. A large and diverse dataset of 139 compounds was collected, including those from the literature and in house from Genentech. The results of this study confirm that the Poulin et al. method is robust and showed the greatest accuracy as compared with the other IVIVE methods in the majority of prediction scenarios studied here. The difference across the prediction methods is most pronounced for (a) albumin-bound drugs, (b) highly bound drugs, and (c) low CL drugs. Predictions of CL showed relevant interspecies differences for BDDCS class 2 compounds; the direct scaling method showed the greatest predictivity for these compounds, particularly for a reduced dataset in rat that have unexpectedly high CL in vivo. This result is a reflection of the direct scaling method's natural tendency to overpredict the true metabolic CL. Overall, this study should facilitate the use of IVIVE correlation methods in physiologically based pharmacokinetics (PBPK) model.

In vitro-in vivo extrapolation of clearance: modeling hepatic metabolic clearance of highly bound drugs and comparative assessment with existing calculation methods

In vitro-in vivo extrapolation (IVIVE) is an important method for estimating the hepatic metabolic clearance (CL) of drugs. This study highlights a problematic area observed when using microsomal data to predict in vivo CL of drugs that are highly bound to plasma proteins, and further explores mechanisms for human CL predictions by associating additional processes to IVIVE disconnect. Therefore, this study attempts to develop a novel IVIVE calculation method, which consists of adjusting the binding terms in a well-stirred liver model. A comparative assessment between the IVIVE method proposed here and previously published methods of Obach (1999. Drug Metab Dispos 27:1350-1359) and Berezhkovskiy (2010. J Pharm Sci 100:1167-1783) was also performed. The assessment was confined by the availability of measured in vitro and in vivo data in humans for 25 drugs highly bound to plasma proteins, for which it can be assumed that metabolism is the major route of elimination. Here, we argue that a difference in drug ionization and binding proteins such as albumin (AL) and alpha-1-acid glycoprotein (AAG) in plasma and liver also needs to be considered in IVIVE based on mechanistic studies. Therefore, converting unbound fraction in plasma to liver essentially increased the predicted CL values, which resulted in much more accurate estimates of in vivo CL as compared with the other IVIVE methods tested. The impact on CL estimate was more apparent for drugs binding to AL than to AAG. This is a mechanistic rational for explaining a considerable proportion of the divergence between previously estimated and observed CL values. Human CL was predicted within 1.5-fold, twofold, and threefold of the observed CL for 84%, 96%, and 100% of the compounds, respectively. Overall, this study demonstrates a significant improvement in the mechanism-based prediction of metabolic CL for these 25 highly bound drugs from in vitro data determined with microsomes, which should facilitate the application of physiologically based pharmacokinetic (PBPK) models in drug discovery and development.