A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding

Optimization of Physicochemical Properties of Pyrrolidine GPR40 AgoPAMs Results in a Differentiated Profile with Improved Pharmacokinetics and Reduced Off-Target Activities

GPR40 AgoPAMs are highly effective antidiabetic agents that have a dual mechanism of action, stimulating both glucose-dependent insulin and GLP-1 secretion. The early lipophilic, aromatic pyrrolidine and dihydropyrazole GPR40 AgoPAMs from our laboratory were highly efficacious in lowering plasma glucose levels in rodents but possessed off-target activities and triggered rebound hyperglycemia in rats at high doses. A focus on increasing molecular complexity through saturation and chirality in combination with reducing polarity for the pyrrolidine AgoPAM chemotype resulted in the discovery of compound 46, which shows significantly reduced off-target activities as well as improved aqueous solubility, rapid absorption, and linear PK. In vivo, compound 46 significantly lowers plasma glucose levels in rats during an oral glucose challenge yet does not demonstrate the reactive hyperglycemia effect at high doses that was observed with earlier GPR40 AgoPAMs.

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Altered Bioavailability and Pharmacokinetics in Crohn's Disease: Capturing Systems Parameters for PBPK to Assist with Predicting the Fate of Orally Administered Drugs

Backgrond and Objective

Crohn’s disease (CD) is a chronic inflammatory bowel disease that affects a wide age range. Hence, CD patients receive a variety of drugs over their life beyond those used for CD itself. The changes to the integrity of the intestine and its drug metabolising enzymes and transporters (DMETs) can alter the oral bioavailability of drugs. However, there are other changes in systems parameters determining the fate of drugs in CD, and understanding these is essential for dose adjustment in patients with CD.

Methods

The current analysis gathered all the available clinical data on the kinetics of drugs in CD (by March 2021), focusing on orally administered small molecule drugs. A meta-analysis of the systems parameters affecting oral drug pharmacokinetics was conducted. The systems information gathered on intestine, liver and blood proteins and other physiological parameters was incorporated into a physiologically based pharmacokinetic (PBPK) platform to create a virtual population of CD patients, with a view for guiding dose adjustment in the absence of clinical data in CD.

Results

There were no uniform trends in the reported changes in reported oral bioavailability. The nature of the drug as well as the formulation affected the direction and magnitude of variation in kinetics in CD patients relative to healthy volunteers. Even for the same drug, the reported changes in exposure varied, possibly due to a lack of distinction between the activity states of CD. The highest alteration was seen with S-verapamil and midazolam, 8.7- and 5.3-fold greater exposure, respectively, in active CD patients relative to healthy volunteers. Only one report was available on liver DMETs in CD, and indicated reduced CYP3A4 activity. In a number of reports, mRNA expression of DMETs in the ileum and colon of CD patients was measured, focussing on P-glycoprotein (p-gp) transporter and CYP3A4 enzyme, and showed contradictory results. No data were available on protein expression in duodenum and jejunum despite their dominant role in oral drug absorption.

Conclusion

There are currently inadequate dedicated clinical or quantitative proteomic studies in CD to enable predictive PBPK models with high confidence and adequate verification. The PBPK models for CD with the available systems parameters were able to capture the major physiological influencers and the gaps to be filled by future research. Quantification of DMETs in the intestine and the liver in CD is warranted, alongside well-defined clinical drug disposition studies with a number of index drugs as biomarkers of changes in DMETs in these patients, to avoid large-scale dedicated studies for every drug to determine the effects of disease on the drug’s metabolism and disposition and the consequential safety and therapeutic concerns.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-022-01169-4.

Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations

The physiologically based pharmacokinetic (PBPK) approach can be used to develop mathematical models for predicting the absorption, distribution, metabolism, and elimination (ADME) of administered drugs in virtual human populations. Haloperidol is a typical antipsychotic drug with a narrow therapeutic index and is commonly used in the management of several medical conditions, including psychotic disorders. Due to the large interindividual variability among patients taking haloperidol, it is very likely for them to experience either toxic or subtherapeutic effects. We intend to develop a haloperidol PBPK model for identifying the potential sources of pharmacokinetic (PK) variability after intravenous and oral administration by using the population-based simulator, PK-Sim. The model was initially developed and evaluated to predict the PK of haloperidol and its reduced metabolite in adult healthy population after intravenous and oral administration. After evaluating the developed PBPK model in healthy adults, it was used to predict haloperidol–rifampicin drug–drug interaction and was extended to tuberculosis patients. The model evaluation was performed using visual assessments, prediction error, and mean fold error of the ratio of the observed-to-predicted values of the PK parameters. The predicted PK values were in good agreement with the corresponding reported values. The effects of the pathophysiological changes and enzyme induction associated with tuberculosis and its treatment, respectively, on haloperidol PK, have been predicted precisely. For all clinical scenarios that were evaluated, the predicted values were within the acceptable two-fold error range.

A Permeability- and Perfusion-based PBPK model for Improved Prediction of Concentration-time Profiles

To improve predictions of concentration‐time (C‐t) profiles of drugs, a new physiologically based pharmacokinetic modeling framework (termed ‘PermQ’) has been developed. This model includes permeability into and out of capillaries, cell membranes, and intracellular lipids. New modeling components include (i) lumping of tissues into compartments based on both blood flow and capillary permeability, and (ii) parameterizing clearances in and out of membranes with apparent permeability and membrane partitioning values. Novel observations include the need for a shallow distribution compartment particularly for bases. C‐t profiles were modeled for 24 drugs (7 acidic, 5 neutral, and 12 basic) using the same experimental inputs for three different models: Rodgers and Rowland (RR), a perfusion‐limited membrane‐based model (K_p,mem), and PermQ. K_p,mem and PermQ can be directly compared since both models have identical tissue partition coefficient parameters. For the 24 molecules used for model development, errors in V_ss and t_1/2 were reduced by 37% and 43%, respectively, with the PermQ model. Errors in C‐t profiles were reduced (increased EOC) by 43%. The improvement was generally greater for bases than for acids and neutrals. Predictions were improved for all 3 models with the use of parameters optimized for the PermQ model. For five drugs in a test set, similar results were observed. These results suggest that prediction of C‐t profiles can be improved by including capillary and cellular permeability components for all tissues.

Using Partition Analysis as a Facile Method to Derive Net Clearances

Partition analysis has been described previously by W.W. Cleland to derive net rate constants and simplify the derivation of enzyme kinetic equations. Here, we show that partition analysis can be used to derive elimination and transfer (distribution) net clearances for use in pharmacokinetic models. For elimination clearances, the net clearance approach is exemplified with a mammillary two‐compartment model with peripheral elimination, and the established well‐stirred and full hepatic clearance models. The intrinsic hepatic clearance associated with an observed average hepatic clearance can be easily calculated with net clearances. Expressions for net transfer clearances are easily derived, including models with explicit membranes (e.g., monolayer permeability and blood–brain barrier models). Together, these approaches can be used to derive equations for physiologically based and hybrid compartmental/ physiologically based models. This tutorial describes how net clearances can be used to derive relationships for simple models as well as increasingly complex models, such as inclusion of active transport and target mediated processes.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Physiologically-Based Pharmacokinetic Modeling Approaches for Patients with SARS-CoV-2 Infection: A Case Study with Imatinib

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection, which causes coronavirus disease 2019 (COVID‐19), manifests as mild respiratory symptoms to severe respiratory failure and is associated with inflammation and other physiological changes. Of note, substantial increases in plasma concentrations of α₁‐acid‐glycoprotein and interleukin‐6 have been observed among patients admitted to the hospital with advanced SARS‐CoV‐2 infection. A physiologically based pharmacokinetic (PBPK) approach is a useful tool to evaluate and predict disease‐related changes on drug pharmacokinetics. A PBPK model of imatinib has previously been developed and verified in healthy people and patients with cancer. In this study, the PBPK model of imatinib was successfully extrapolated to patients with SARS‐CoV‐2 infection by accounting for disease‐related changes in plasma α₁‐acid‐glycoprotein concentrations and the potential drug interaction between imatinib and dexamethasone. The model demonstrated a good predictive performance in describing total and unbound imatinib concentrations in patients with SARS‐CoV‐2 infection. PBPK simulations highlight that an equivalent dose of imatinib may lead to substantially higher total drug concentrations in patients with SARS‐CoV‐2 infection compared to that in patients with cancer, while the unbound concentrations remain comparable between the 2 patient populations. This supports the notion that unbound trough concentration is a better exposure metric for dose adjustment of imatinib in patients with SARS‐CoV‐2 infection, compared to the corresponding total drug concentration. Potential strategies for refinement and generalization of the PBPK modeling approach in the patient population with SARS‐CoV‐2 are also provided in this article, which could be used to guide study design and inform dose adjustment in the future.

The blood-to-plasma ratio and predicted GABAA-binding affinity of designer benzodiazepines

PURPOSE

The number of benzodiazepines appearing as new psychoactive substances (NPS) is continually increasing. Information about the pharmacological parameters of these compounds is required to fully understand their potential effects and harms. One parameter that has yet to be described is the blood-to-plasma ratio. Knowledge of the pharmacodynamics of designer benzodiazepines is also important, and the use of quantitative structure-activity relationship (QSAR) modelling provides a fast and inexpensive method of predicting binding affinity to the GABA_A receptor.

METHODS

In this work, the blood-to-plasma ratios for six designer benzodiazepines (deschloroetizolam, diclazepam, etizolam, meclonazepam, phenazepam, and pyrazolam) were determined. A previously developed QSAR model was used to predict the binding affinity of nine designer benzodiazepines that have recently appeared.

RESULTS

Blood-to-plasma values ranged from 0.57 for phenazepam to 1.18 to pyrazolam. Four designer benzodiazepines appearing since 2017 (fluclotizolam, difludiazepam, flualprazolam, and clobromazolam) had predicted binding affinities to the GABA_A receptor that were greater than previously predicted binding affinities for other designer benzodiazepines.

CONCLUSIONS

This work highlights the diverse nature of the designer benzodiazepines and adds to our understanding of their pharmacology. The greater predicted binding affinities are a potential indication of the increasing potency of designer benzodiazepines appearing on the illicit drugs market.

A Physiologically Based Pharmacokinetic Model for Predicting Diazepam Pharmacokinetics after Intravenous, Oral, Intranasal, and Rectal Applications

Diazepam is one of the most prescribed anxiolytic and anticonvulsant that is administered through intravenous (IV), oral, intramuscular, intranasal, and rectal routes. To facilitate the clinical use of diazepam, there is a need to develop formulations that are convenient to administer in ambulatory settings. The present study aimed to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for diazepam that is capable of predicting its pharmacokinetics (PK) after IV, oral, intranasal, and rectal applications using a whole-body population-based PBPK simulator, Simcyp^®. The model evaluation was carried out using visual predictive checks, observed/predicted ratios (R_obs/pred), and the average fold error (AFE) of PK parameters. The Diazepam PBPK model successfully predicted diazepam PK in an adult population after doses were administered through IV, oral, intranasal, and rectal routes, as the R_obs/pred of all PK parameters were within a two-fold error range. The developed model can be used for the development and optimization of novel diazepam dosage forms, and it can be extended to simulate drug response in situations where no clinical data are available (healthy and disease).

Camptothecin shows better promise than Curcumin in the inhibition of the Human Telomerase: A computational study

OBJECTIVES

The Human Telomerase enzyme has become a drug target in the treatment of cancers and age-related disorders. This study aims to identify potential natural inhibitors of the Human Telomerase from compounds derived from edible African plants.

MATERIALS AND METHODS

A library of 1,126 natural compounds was molecularly docked against the Telomerase Reverse Transcriptase (PDB ID: 5ugw), the catalytic subunit of the target protein. Curcumin, a known Telomerase inhibitor was used as the standard. The front-runner compounds were screened for bioavailability, pharmacokinetic properties, and bioactivity using the SWISSADME, PKCSM, and Molinspiration webservers respectively. The molecular dynamic simulation and analyses of the apo and holo proteins were performed by the Galaxy supercomputing webserver.

RESULTS

The results of the molecular docking and virtual screening reveal Augustamine and Camptothecin as lead compounds. Augustamine has better drug-likeness and pharmacokinetic properties while Camptothecin showed better bioactivity and stronger binding affinity (-8.2 kcal/mol) with the target. The holo structure formed by Camptothecin showed greater inhibitory activity against the target with a total RMSF of 169.853, B-Factor of 20.164, and 108 anti-correlating residues.

CONCLUSION

Though they both act at the same binding site, Camptothecin induces greater Telomerase inhibition and better molecular stability than the standard, Curcumin. Further tests are required to investigate the inhibitory activities of the lead compounds.

Accelerated Repurposing and Drug Development of Pulmonary Hypertension Therapies for COVID-19 Treatment Using an AI-Integrated Biosimulation Platform

The COVID-19 pandemic has reached over 100 million worldwide. Due to the multi-targeted nature of the virus, it is clear that drugs providing anti-COVID-19 effects need to be developed at an accelerated rate, and a combinatorial approach may stand to be more successful than a single drug therapy. Among several targets and pathways that are under investigation, the renin-angiotensin system (RAS) and specifically angiotensin-converting enzyme (ACE), and Ca²⁺-mediated SARS-CoV-2 cellular entry and replication are noteworthy. A combination of ACE inhibitors and calcium channel blockers (CCBs), a critical line of therapy for pulmonary hypertension, has shown therapeutic relevance in COVID-19 when investigated independently. To that end, we conducted in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the CCBs and ACEi compounds to tissues implicated in COVID-19 pathogenesis.

Whole blood or plasma: what is the ideal matrix for pharmacokinetic-driven drug candidate selection?

In the present era of drug development, quantification of drug concentrations following pharmacokinetic studies has preferentially been performed using plasma as a matrix rather than whole blood. However, it is critical to realize the difference between measuring drug concentrations in blood versus plasma and the consequences thereof. Pharmacokinetics using plasma data may be misleading if concentrations differ between plasma and red blood cells (RBCs) because of differential binding in blood. In this review, factors modulating the partitioning of drugs into RBCs are discussed and the importance of determining RBC uptake of drugs for drug candidate selection is explored. In summary, the choice of matrix (plasma vs whole blood) is an important consideration to be factored in during drug discovery.

An UHPLC-TOF-MS method for quantifying novel brominated anticancer compound bromophenol-thiosemicarbazone hybrid and its application to in vivo pharmacokinetic study

Background

Bromophenol-thiosemicarbazone hybrid was a novel synthetic brominated anticancer compound with two bromine atoms. Bromophenol-thiosemicarbazone hybrid showed considerable selective inhibitory activity against PARP1 (IC50 = 29.5 nmol/L). UHPLC-TOF-MS was used to establish a new method to quantify bromophenol-thiosemicarbazone hybrid bio-samples for indispensable quantitation analysis in further extensive pre-clinical studies.

Methods

Chromatographic and mass spectrometry parameters were optimized for quantitative method establishment. Improved protein-precipitated method was applied to the extraction of bromophenol-thiosemicarbazone hybrid in rat plasma samples. Furthermore, this proposed method was applied to an intravenous bolus dose to male rats.

Results

Mobile phase was consisted of water for A and acetonitrile for B with 25 mmol/L formic acid in both A and B. The flow rate was 0.30 mL/min, and the run time of bromophenol-thiosemicarbazone hybrid was 4.0 min. A Thermo Fisher Accucore 2.6 μm C18 column (50 × 2.1 mm i.d.; San Jose, USA) was used for chromatographic separation. High resolution mass spectrometry was used to quantify samples by exact mass number of compound which was operated on negative ionization mode. Linear dynamic range of the established method was widely with 13.7–10000 nmol/L. Pharmacokinetics properties of bromophenol-thiosemicarbazone hybrid were shown in the results.

Conclusion

This method was reliable and reproducible from sample preparation to analysis and storage stability under the investigated conditions. It may be useful for analysis of halogenated compounds and brominated compounds in ultra-performance liquid chromatography-mass spectrometry.

Predicting topical drug clearance from the skin

For topical drug products that target sites of action in the viable epidermal and/or upper dermal compartment of the skin, the local concentration profiles have proven difficult to quantify because drug clearance from the viable cutaneous tissue is not well characterised. Without such knowledge, of course, it is difficult-if not impossible-to predict a priori whether and over what time frame a topical formulation will permit an effective concentration of drug within the skin 'compartment' to be achieved. Here, we test the hypothesis that valuable information about drug disposition, and specifically its clearance, in this experimentally difficult-to-access compartment (at least, in vivo) can be derived from available systemic pharmacokinetic data for drugs administered via transdermal delivery systems. A multiple regression analysis was undertaken to determine the best-fit empirical correlation relating clearance from the skin to known or easily calculable drug properties. It was possible, in this way, to demonstrate a clear relationship between drug clearance from the skin and key physical chemical properties of the drug (molecular weight, log P and topological polar surface area). It was further demonstrated that values predicted by the model correlated well with those derived from in vitro skin experiments.

Application of the Extended Clearance Classification System (ECCS) in Drug Discovery and Development: Selection of Appropriate In Vitro Tools and Clearance Prediction

In vitro to in vivo extrapolation (IVIVE) to predict human hepatic clearance, including metabolism and transport, requires extensive experimental resources. In addition, there may be technical challenges to measure low clearance values. Therefore, prospective identification of rate-determining step(s) in hepatic clearance through application of the Extended Clearance Classification System (ECCS) could be beneficial for optimal compound characterization. IVIVE for hepatic intrinsic clearance (CLint,h) prediction is conducted for a set of 36 marketed drugs with low-to-high in vivo clearance, which are substrates of metabolic enzymes and active uptake transporters in the liver. The compounds were assigned to the ECCS classes, and CLint,h, estimated with HepatoPac (a micropatterned hepatocyte coculture system), was compared with values calculated based on suspended hepatocyte incubates. An apparent permeability threshold (apical to basal) of 50 nm/s in LLC-PK1 cells proved optimal for ECCS classification. A reasonable performance of the IVIVE for compounds across multiple classes using HepatoPac was achieved (with 2-3-fold error), except for substrates of uptake transporters (class 3b), for which scaling of uptake clearance using plated hepatocytes is more appropriate. Irrespective of the ECCS assignment, metabolic clearance can be estimated well using HepatoPac. The validation and approach elaborated in the present study can result in proposed decision trees for the selection of the optimal in vitro assays guided by ECCS class assignment, to support compound optimization and candidate selection. SIGNIFICANCE STATEMENT: Characterization of the rate-determining step(s) in hepatic elimination could be on the critical path of compound optimization during drug discovery. This study demonstrated that HepatoPac and plated hepatocytes are suitable tools for the estimation of metabolic and active uptake clearance, respectively, for a larger set of marketed drugs, supporting a comprehensive strategy to select optimal in vitro tools and to achieve Extended Clearance Classification System-dependent in vitro to in vivo extrapolation for human clearance prediction.

Prediction of Tissue-Plasma Partition Coefficients Using Microsomal Partitioning: Incorporation into Physiologically based Pharmacokinetic Models and Steady-State Volume of Distribution Predictions

Drug distribution is a necessary component of models to predict human pharmacokinetics. A new membrane-based tissue-plasma partition coefficient (K p) method (K p,mem) to predict unbound tissue to plasma partition coefficients (K pu) was developed using in vitro membrane partitioning [fraction unbound in microsomes (f um)], plasma protein binding, and log P The resulting K p values were used in a physiologically based pharmacokinetic (PBPK) model to predict the steady-state volume of distribution (V ss) and concentration-time (C-t) profiles for 19 drugs. These results were compared with K p predictions using a standard method [the differential phospholipid K p prediction method (K p,dPL)], which differentiates between acidic and neutral phospholipids. The K p,mem method was parameterized using published rat K pu data and tissue lipid composition. The K pu values were well predicted with R 2 = 0.8. When used in a PBPK model, the V ss predictions were within 2-fold error for 12 of 19 drugs for K p,mem versus 11 of 19 for Kp,dPL With one outlier removed for K p,mem and two for K p,dPL, the V ss predictions for R 2 were 0.80 and 0.79 for the K p,mem and K p,dPL methods, respectively. The C-t profiles were also predicted and compared. Overall, the K p,mem method predicted the V ss and C-t profiles equally or better than the K p,dPL method. An advantage of using f um to parameterize membrane partitioning is that f um data are used for clearance prediction and are, therefore, generated early in the discovery/development process. Also, the method provides a mechanistically sound basis for membrane partitioning and permeability for further improving PBPK models. SIGNIFICANCE STATEMENT: A new method to predict tissue-plasma partition coefficients was developed. The method provides a more mechanistic basis to model membrane partitioning.

Methods to Predict Volume of Distribution

PURPOSE OF REVIEW

Prior to human studies, knowledge of drug disposition in the body is useful to inform decisions on drug safety and efficacy, first in human dosing, and dosing regimen design. It is therefore of interest to develop predictive models for primary pharmacokinetic parameters, clearance, and volume of distribution. The volume of distribution of a drug is determined by the physiological properties of the body and physiochemical properties of the drug, and is used to determine secondary parameters, including the half-life. The purpose of this review is to provide an overview of current methods for the prediction of volume of distribution of drugs, discuss a comparison between the methods, and identify deficiencies in current predictive methods for future improvement.

RECENT FINDINGS

Several volumes of distribution prediction methods are discussed, including preclinical extrapolation, physiological methods, tissue composition-based models to predict tissue:plasma partition coefficients, and quantitative structure-activity relationships. Key factors that impact the prediction of volume of distribution, such as permeability, transport, and accuracy of experimental inputs, are discussed. A comparison of current methods indicates that in general, all methods predict drug volume of distribution with an absolute average fold error of 2-fold. Currently, the use of composition-based PBPK models is preferred to models requiring in vivo input.

SUMMARY

Composition-based models perfusion-limited PBPK models are commonly used at present for prediction of tissue:plasma partition coefficients and volume of distribution, respectively. A better mechanistic understanding of important drug distribution processes will result in improvements in all modeling approaches.

Relevance of In Vitro Metabolism Models to PET Radiotracer Development: Prediction of In Vivo Clearance in Rats from Microsomal Stability Data

The prediction of in vivo clearance from in vitro metabolism models such as liver microsomes is an established procedure in drug discovery. The potentials and limitations of this approach have been extensively evaluated in the pharmaceutical sector; however, this is not the case for the field of positron emission tomography (PET) radiotracer development. The application of PET radiotracers and classical drugs differs greatly with regard to the amount of substance administered. In typical PET imaging sessions, subnanomolar quantities of the radiotracer are injected, resulting in body concentrations that cannot be readily simulated in analytical assays. This raises concerns regarding the predictability of radiotracer clearance from in vitro data. We assessed the accuracy of clearance prediction for three prototypical PET radiotracers developed for imaging the A₁ adenosine receptor (A₁AR). Using the half-life (t_1/2) approach and physiologically based scaling, in vivo clearance in the rat model was predicted from microsomal stability data. Actual clearance could be accurately predicted with an average fold error (AFE) of 0.78 and a root mean square error (RMSE) of 1.6. The observed slight underprediction (1.3-fold) is in accordance with the prediction accuracy reported for classical drugs. This result indicates that the prediction of radiotracer clearance is possible despite concentration differences of more than three orders of magnitude between in vitro and in vivo conditions. Consequently, in vitro metabolism models represent a valuable tool for PET radiotracer development.

The Promise of Long-Acting Antiretroviral Therapies: From Need to Manufacture

Antiretroviral therapy has transformed human immunodeficiency virus infections from certain death to a manageable chronic disease. Achieving strict adherence to drug regimens that limit toxicities and viral resistance is an achievable goal. Success is defined by halting viral transmission and by continuous viral restriction. A step towards improving treatment outcomes is in long-acting antiretrovirals. While early results remain encouraging there remain opportunities for improvement. These rest, in part, on the required large drug dosing volumes, local injection-site reactions, and frequency of injections. Thus, implantable devices and long-acting parenteral prodrugs have emerged which may provide more effective clinical outcomes. The recent successes in transforming native antiretrovirals into lipophilic and hydrophobic prodrugs stabilized into biocompatible surfactants can positively affect both. Formulating antiretroviral prodrugs demonstrates improvements in cell and tissue targeting, in drug-dosing intervals, and in the administered volumes of nanosuspensions. As such, the newer formulations also hold the potential to suppress viral loads beyond more conventional therapies with the ultimate goal of HIV-1 elimination when combined with other modalities.

Effective cellular internalization, cell cycle arrest and improved pharmacokinetics of Tamoxifen by cholesterol based lipopolymeric nanoparticles

The present study aims at the development of cholesterol based lipopolymeric nanoparticles for improved entrapment, better cell penetration and improved pharmacokinetics of Tamoxifen (TMX). Self-assembling cholesterol grafted lipopolymer, mPEG-b-(CB-{g-chol}-co-LA) was synthesized from poly(ethyleneglycol)-block-2-methyl-2-carboxyl-propylenecarboxylic acid-co-poly (l-lactide) [mPEG-b-(CB-{g-COOH}-co-LA)] copolymer followed by carbodiimide coupling for attaching cholesterol. Lipopolymeric nanoparticles were prepared using o/w solvent evaporation technique, which were subsequently characterized to determine its particle size, entrapment efficiency, release pattern and compared with mPEG-PLA nanoparticles. Further, in order to assess the in vitro efficacy, cytotoxicity studies, uptake, apoptosis assay and cell cycle analysis were performed in breast cancer cell lines (MCF-7 and 4T1). Finally, the pharmacokinetic profile of TMX loaded mPEG-b-(CB-{g-chol}-co-LA) lipopolymeric nanoparticles was also performed. TMX loaded lipopolymeric nanoparticles of particle size 151.25 ± 3.74 (PDI 0.123) and entrapment efficiency of 73.62 ± 3.08% were formulated. The haemolytic index, protein binding and in vitro drug release of the optimized nanoparticles were found to be comparable to that of the TMX loaded mPEG-PLA nanoparticles. Lipopolymeric nanoparticles demonstrated improved IC₅₀ values in breast cancer cells (22.2 μM in 4T1; 18.8 μM in MCF-7) than free TMX (27.6 μM and 23.5 μM respectively) and higher uptake efficiency. At IC₅₀ values, TMX loaded lipopolymeric nanoparticles induced apoptosis and cell cycle arrest (G₀/G₁ phase) to similar extent as that of free drug. Pharmacokinetic studies indicated ∼2.5-fold increase in the half-life (t_1/2) (p < 0.001) and ∼2.7-fold (p < 0.001) increase in the mean residence time (MRT) of TMX following incorporation into lipopolymeric nanoparticles. Thus, mPEG-b-(CB-{g-chol}-co-LA) lipopolymeric nanoparticles offer a more promising approach for delivery of Tamoxifen in breast cancer by improving drug internalization and prolonging the mean residence time of the drug indicating possibility of dose reduction and hence bypassing the adverse effects of TMX therapy.

Integration of a plasma protein binding factor to the Chemical-Specific Adjustment Factor (CSAF) for facilitating the estimation of uncertainties in interspecies extrapolations when deriving health-based exposure limits for active pharmaceutical ingredients: Investigation of recent drug datasets

The objective was to challenge cross-species extrapolation factors with which to scale animal doses to human by any route for non-carcinogenic endpoints. The conventional hypothesis of the toxicokinetics (TK)-toxicodynamics (TD) relationship was equal toxicity at equal plasma level of the total drug moiety in each species, but this should also follow the free drug assumption, which states that only the unbound drug moiety in plasma may elicit a TD effect in tissue. Therefore, a protein binding factor (PBF) was combined with the Chemical-Specific Adjustment Factor (CSAF) (i.e., CSAF x PBF). The value of PBF of each drug was set equal to the ratio between human and animals of the unbound fraction in plasma (fu_p). Recent drug datasets were investigated. Our results indicate that any CSAF value would be increased or decreased while PBF deviates to the unity, and this required more attention. Accordingly, further testing indicated that the CSAF values set equal to basic allometric uncertainty factors according to the conventional hypothesis (dog∼2, monkey∼3.1, rat∼7, mouse∼12) would increase by including PBF for 30% of the drugs tested that showed a superior fu_p value in human compared to animals. However, default uncertainty factors in the range of 10-100 were less frequently exceeded. Overall, PBF could be combined with any other uncertainty factor to get reliable estimate of CSAF for each bound drug in deriving health-based exposure limits.

On the Nature of Physiologically-Based Pharmacokinetic Models -A Priori or A Posteriori? Mechanistic or Empirical?

Physiologically-based pharmacokinetic (PBPK) models explicitly incorporate tissue-specific blood flows, partition coefficients, and metabolic processes. Since PBPK models are derived using physiologic parameters and interactions of the compound with tissue components, these models are considered to be "bottom up" as opposed to "top down". Modeling approaches can be characterized as either a posteriori (observational) or a priori (based solely on theory). Furthermore, approaches can be mechanistic (structure and components based on mechanisms) or empirical (based on observations alone). Both "bottom up" and "top down" approaches can incorporate either empirical or mechanistic components. In this perspective, we discuss some of the methods and assumptions of current PBPK modeling approaches. Specifically, we discuss drug partitioning into phospholipids and neutral lipids, use of blood-plasma ratios to estimate basic drug tissue partitioning, and clearance of neutral and acidic drugs. Based on these discussions, we believe that current PBPK models are mechanistic but a posteriori and semi-empirical.