Prediction of drug disposition in diabetic patients by means of a physiologically based pharmacokinetic model

A physiologically-based quantitative systems pharmacology model for mechanistic understanding of the response to alogliptin and its application in patients with renal impairment

Alogliptin is a highly selective inhibitor of dipeptidyl peptidase-4 and primarily excreted as unchanged drug in the urine, and differences in clinical outcomes in renal impairment patients increase the risk of serious adverse reactions. In this study, we developed a comprehensive physiologically-based quantitative systematic pharmacology model of the alogliptin-glucose control system to predict plasma exposure and use glucose as a clinical endpoint to prospectively understand its therapeutic outcomes with varying renal function. Our model incorporates a PBPK model for alogliptin, DPP-4 activity described by receptor occupancy theory, and the crosstalk and feedback loops for GLP-1-GIP-glucagon, insulin, and glucose. Based on the optimization of renal function-dependent parameters, the model was extrapolated to different stages renal impairment patients. Ultimately our model adequately describes the pharmacokinetics of alogliptin, the progression of DPP-4 inhibition over time and the dynamics of the glucose control system components. The extrapolation results endorse the dose adjustment regimen of 12.5 mg once daily for moderate patients and 6.25 mg once daily for severe and ESRD patients, while providing additional reflections and insights. In clinical practice, our model could provide additional information on the in vivo fate of DPP4 inhibitors and key regulators of the glucose control system.

Development of a Physiologically Based Pharmacokinetic Population Model for Diabetic Patients and its Application to Understand Disease-drug-drug Interactions

INTRODUCTION

The activity changes of cytochrome P450 (CYP450) enzymes, along with the complicated medication scenarios in diabetes mellitus (DM) patients, result in the unanticipated pharmacokinetics (PK), pharmacodynamics (PD), and drug-drug interactions (DDIs). Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool for assessing the influence of disease status on CYP enzymes and the resulting DDIs. This work aims to develop a novel diabetic PBPK population model to facilitate the prediction of PK and DDI in DM patients.

METHODS

First, mathematical functions were constructed to describe the demographic and non-CYP physiological characteristics specific to DM, which were then incorporated into the PBPK model to quantify the net changes in CYP enzyme activities by comparing the PK of CYP probe drugs in DM versus non-DM subjects.

RESULTS

The results show that the enzyme activity is reduced by 32.3% for CYP3A4/5, 39.1% for CYP2C19, and 27% for CYP2B6, while CYP2C9 activity is enhanced by 38% under DM condition. Finally, the diabetic PBPK model was developed through integrating the DM-specific CYP activities and other parameters and was further used to perform PK simulations under 12 drug combination scenarios, among which 3 combinations were predicted to result in significant PK changes in DM, which may cause DDI risks in DM patients.

CONCLUSIONS

The PBPK modeling applied herein provides a quantitative tool to assess the impact of disease factors on relevant enzyme pathways and potential disease-drug-drug-interactions (DDDIs), which may be useful for dosing regimen optimization and minimizing the DDI risks associated with the treatment of DM.

Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods

Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.

Physiologically Based Pharmacokinetic Modelling to Identify Physiological and Drug Parameters Driving Pharmacokinetics in Obese Individuals

BACKGROUND

Obese individuals are often underrepresented in clinical trials, leading to a lack of dosing guidance.

OBJECTIVE

This study aimed to investigate which physiological parameters and drug properties determine drug disposition changes in obese using our physiologically based pharmacokinetic (PBPK) framework, informed with obese population characteristics.

METHODS

Simulations were performed for ten drugs with clinical data in obese (i.e., midazolam, triazolam, caffeine, chlorzoxazone, acetaminophen, lorazepam, propranolol, amikacin, tobramycin, and glimepiride). PBPK drug models were developed and verified first against clinical data in non-obese (body mass index (BMI) ≤ 30 kg/m²) and subsequently in obese (BMI ≥ 30 kg/m²) without changing any drug parameters. Additionally, the PBPK model was used to study the effect of obesity on the pharmacokinetic parameters by simulating drug disposition across BMI, starting from 20 up to 60 kg/m².

RESULTS

Predicted pharmacokinetic parameters were within 1.25-fold (71.5%), 1.5-fold (21.5%) and twofold (7%) of clinical data. On average, clearance increased by 1.6% per BMI unit up to 64% for a BMI of 60 kg/m², which was explained by the increased hepatic and renal blood flows. Volume of distribution increased for all drugs up to threefold for a BMI of 60 kg/m²; this change was driven by pK_a for ionized drugs and logP for neutral and unionized drugs. C_max decreased similarly across all drugs while t_max remained unchanged.

CONCLUSION

Both physiological changes and drug properties impact drug pharmacokinetics in obese subjects. Clearance increases due to enhanced hepatic and renal blood flows. Volume of distribution is higher for all drugs, with differences among drugs depending on their pK_a/logP.

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug-Drug-Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

Background

Metformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug–drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor.

Objective

The objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug–gene interaction, the cimetidine-metformin drug–drug interaction, and the impact of renal impairment on metformin exposure.

Methods

Physiologically based pharmacokinetic models were developed in PK-Sim^® (version 8.0). Thirty-nine clinical studies (dosing range 0.001–2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug–drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100–800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development.

Results

The established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug–gene interaction, drug–drug interaction, and drug–drug–gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug–drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species.

Conclusions

Whole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug–gene interaction, drug–drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients.

Electronic supplementary material

The online version of this article (10.1007/s40262-020-00896-w) contains supplementary material, which is available to authorized users.

A Whole-Body Physiologically Based Pharmacokinetic Model Characterizing Interplay of OCTs and MATEs in Intestine, Liver and Kidney to Predict Drug-Drug Interactions of Metformin with Perpetrators

Transmembrane transport of metformin is highly controlled by transporters including organic cation transporters (OCTs), plasma membrane monoamine transporter (PMAT), and multidrug/toxin extrusions (MATEs). Hepatic OCT1, intestinal OCT3, renal OCT2 on tubule basolateral membrane, and MATE1/2-K on tubule apical membrane coordinately work to control metformin disposition. Drug–drug interactions (DDIs) of metformin occur when co-administrated with perpetrators via inhibiting OCTs or MATEs. We aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model characterizing interplay of OCTs and MATEs in the intestine, liver, and kidney to predict metformin DDIs with cimetidine, pyrimethamine, trimethoprim, ondansetron, rabeprazole, and verapamil. Simulations showed that co-administration of perpetrators increased plasma exposures to metformin, which were consistent with clinic observations. Sensitivity analysis demonstrated that contributions of the tested factors to metformin DDI with cimetidine are gastrointestinal transit rate > inhibition of renal OCT2 ≈ inhibition of renal MATEs > inhibition of intestinal OCT3 > intestinal pH > inhibition of hepatic OCT1. Individual contributions of transporters to metformin disposition are renal OCT2 ≈ renal MATEs > intestinal OCT3 > hepatic OCT1 > intestinal PMAT. In conclusion, DDIs of metformin with perpetrators are attributed to integrated effects of inhibitions of these transporters.

Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues

Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans’ known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T_1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T_1/2 and muscle, kidney, and red blood cells that have longer T_1/2. The highest maximal concentrations (C_max) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.

Physiologically-Based Pharmacokinetic-Pharmacodynamics Model Characterizing CYP2C19 Polymorphisms to Predict Clopidogrel Pharmacokinetics and Its Anti-Platelet Aggregation Effect Following Oral Administration to Coronary Artery Disease Patients With or Without Diabetes

Background and Objective: Clopidogrel (CLOP) is commonly used in coronary artery disease (CAD) patients with or without diabetes (DM), but these patients often suffer CLOP resistance, especially those with diabetes. This study was aimed to develop a physiologically-based pharmacokinetic-pharmacodynamic (PBPK-PD) model to describe the pharmacokinetics and pharmacodynamics of clopidogrel active metabolite (CLOP-AM) in CAD patients with or without DM.

Methods: The PBPK-PD model was first established and validated in healthy subjects and then in CAD patients with or without DM. The influences of CYP2C19, CYP2C9, CYP3A4, carboxylesterase 1 (CES1), gastrointestinal transit rates (K_t,i) and platelets response to CLOP-AM (k_irre) on predicted pharmacokinetics and pharmacodynamics were investigated, followed with their individual and integrated effects on CLOP-AM pharmacokinetics due to changes in DM status.

Results: Most predictions fell within 0.5–2.0 folds of observations, indicating successful predictions. Sensitivity analysis showed that contributions of interested factors to pharmacodynamics were CES1> k_irre> K_t,i> CYP2C19 > CYP3A4> CYP2C9. Mimicked analysis showed that the decreased exposure of CLOP-AM by DM was mainly attributed to increased CES1 activity, followed by decreased CYP2C19 activity.

Conclusion: The pharmacokinetics and pharmacodynamics of CLOP-AM were successfully predicted using the developed PBPK-PD model. Clopidogrel resistance by DM was the integrated effects of altered K_t,i, CYP2C19, CYP3A4, CES1 and k_irre.

Oral anti-diabetic drugs as endocrine disruptors in vitro - No evidence for additive effects in binary mixtures

Diabetes is one of the World's most concerning health problems and millions of patients are using anti-diabetic drugs (ADDs) in order to control blood glucose. The in vitro H295R steroidogenesis assay was implemented to investigate endocrine effects of three ADDs, metformin (MET), glimepiride (GLIM), sitagliptin (SIT) and the cholesterol-lowering drug simvastatin (SIM) individually and in three binary mixtures. Steroid hormones were analyzed using LC-MS/MS. Mixture effects were assessed by applying the Concentration Addition (CA) model. All tested drugs and binary mixtures interrupted the H295R steroidogenesis with different potency. The effects of MET:GLIM on the steroidogenesis were overall similar to the steroidogenic profile of GLIM, however effects were less pronounced. The binary mixture of MET:SIT showed overall minor effects on steroid production and only at very high concentrations. The SIM:SIT mixture showed inhibition downstream from cholesterol, which was attributed to the effects of SIM. The CA model partly predicted the effect of MET:SIT on some steroids but significantly overestimated the effects of MET:GLIM and SIM:SIT. Thus, the applicability of the CA model was limited and cocktail effects appeared to be intermediate responses of individual drugs, rather than additive. The complexity of dynamic pathways such as steroidogenesis appears to significantly reduce the use of the CA model. In conclusion, more dynamic models are needed to predict mixture effects in complex systems.

Investigating the Role of Altered Systemic Albumin Concentration on the Disposition of Theophylline in Adult and Pediatric Patients with Asthma by Using the Physiologically Based Pharmacokinetic Approach

Theophylline is commonly used for the treatment of asthma and has a low hepatic clearance. The changes in plasma albumin concentration occurring in asthma may affect the exposure of theophylline. The aim of the presented work was to predict theophylline pharmacokinetics (PK) after incorporating the changes in plasma albumin concentration occurring in patients with asthma into a physiologically based pharmacokinetic (PBPK) model to see whether these changes can affect the systemic theophylline concentrations in asthma. The PBPK model was developed following a systematic model building approach using Simcyp. The predictions were performed initially in healthy adults after intravenous and oral drug administration. Only when the developed adult PBPK model had adequately predicted theophylline PK in healthy adults, the changes in plasma albumin concentrations were incorporated into the model for predicting drug exposure in patients with asthma. After evaluation of the developed model in the adult population, it was scaled to children on physiologic basis. The model evaluation was performed by using visual predictive checks and comparison of ratio of observed and predicted (Robs/Pre) PK parameters along with their 2-fold error range. The developed PBPK model has effectively described theophylline PK in both healthy and diseased populations, as Robs/Pre for all the PK parameters were within the 2-fold error limit. The predictions in patients with asthma showed that there were no significant changes in PK parameters after incorporating the changes in serum albumin concentration. The mechanistic nature of the developed asthma-PBPK model can facilitate its extension to other drugs. SIGNIFICANCE STATEMENT: Exposure of a low hepatic clearance drug like theophylline may be susceptible to plasma albumin concentration changes that occur in asthma. These changes in systemic albumin concentrations can be incorporated into a physiologically based pharmacokinetic model to predict theophylline pharmacokinetics in adult and pediatric asthma populations. The presented work is focused on predicting theophylline absorption, distribution, metabolism, and elimination in adult and pediatric asthma populations after incorporating reported changes in serum albumin concentrations to see their impact on the systemic theophylline concentrations.

Physiologically based pharmacokinetic-pharmacodynamic modeling for prediction of vonoprazan pharmacokinetics and its inhibition on gastric acid secretion following intravenous/oral administration to rats, dogs and humans

Vonoprazan is characterized as having a long-lasting antisecretory effect on gastric acid. In this study we developed a physiologically based pharmacokinetic (PBPK)-pharmacodynamic (PD) model linking to stomach to simultaneously predict vonoprazan pharmacokinetics and its antisecretory effects following administration to rats, dogs, and humans based on in vitro parameters. The vonoprazan disposition in the stomach was illustrated using a limited-membrane model. In vitro metabolic and transport parameters were derived from hepatic microsomes and Caco-2 cells, respectively. We found the most predicted plasma concentrations and pharmacokinetic parameters of vonoprazan in rats, dogs and humans were within twofold errors of the observed data. Free vonoprazan concentrations (f_u × C₂) in the stomach were simulated and linked to the antisecretory effects of the drug (I) (increases in pH or acid output) using the fomula dI/dt = k × f_u × C₂ × (I_max − I) − k_d × I. The vonoprazan dissociation rate constant k_d (0.00246 min⁻¹) and inhibition index K_I (35 nM) for H⁺/K⁺-ATPase were obtained from literatures. The vonoprazan-H⁺/K⁺-ATPase binding rate constant k was 0.07028 min⁻¹· μM⁻¹ using ratio of k_d to K_I. The predicted antisecretory effects were consistent with the observations following intravenous administration to rats (0.7 and 1.0 mg/kg), oral administration to dogs (0.3 and 1.0 mg/kg) and oral single dose or multidose to humans (20, 30, and 40 mg). Simulations showed that vonoprazan concentrations in stomach were 1000-fold higher than those in the plasma at 24 h following administration to human. Vonoprazan pharmacokinetics and its antisecretory effects may be predicted from in vitro data using the PBPK-PD model of the stomach. These findings may highlight 24-h antisecretory effects of vonoprazan in humans following single-dose or the sustained inhibition throughout each 24-h dosing interval during multidose administration.

Imbalance of Drug Transporter-CYP450s Interplay by Diabetes and Its Clinical Significance

The pharmacokinetics of a drug is dependent upon the coordinate work of influx transporters, enzymes and efflux transporters (i.e., transporter-enzyme interplay). The transporter-enzyme interplay may occur in liver, kidney and intestine. The influx transporters involving drug transport are organic anion transporting polypeptides (OATPs), peptide transporters (PepTs), organic anion transporters (OATs), monocarboxylate transporters (MCTs) and organic cation transporters (OCTs). The efflux transporters are P-glycoprotein (P-gp), multidrug/toxin extrusions (MATEs), multidrug resistance-associated proteins (MRPs) and breast cancer resistance protein (BCRP). The enzymes related to drug metabolism are mainly cytochrome P450 enzymes (CYP450s) and UDP-glucuronosyltransferases (UGTs). Accumulating evidence has demonstrated that diabetes alters the expression and functions of CYP450s and transporters in a different manner, disordering the transporter-enzyme interplay, in turn affecting the pharmacokinetics of some drugs. We aimed to focus on (1) the imbalance of transporter-CYP450 interplay in the liver, intestine and kidney due to altered expressions of influx transporters (OATPs, OCTs, OATs, PepTs and MCT6), efflux transporters (P-gp, BCRP and MRP2) and CYP450s (CYP3As, CYP1A2, CYP2E1 and CYP2Cs) under diabetic status; (2) the net contributions of these alterations in the expression and functions of transporters and CYP450s to drug disposition, therapeutic efficacy and drug toxicity; (3) application of a physiologically-based pharmacokinetic model in transporter-enzyme interplay.

Gastrointestinal pain

Gastrointestinal (GI) pain - a form of visceral pain - is common in some disorders, such as irritable bowel syndrome, Crohn's disease and pancreatitis. However, identifying the cause of GI pain frequently represents a diagnostic challenge as the clinical presentation is often blurred by concomitant autonomic and somatic symptoms. In addition, GI pain can be nociceptive, neuropathic and associated with cancer, but in many cases multiple aetiologies coexist in an individual patient. Mechanisms of GI pain are complex and include both peripheral and central sensitization and the involvement of the autonomic nervous system, which has a role in generating the symptoms that frequently accompany pain. Treatment of GI pain depends on the precise type of pain and the primary disorder in the patient but can include, for example, pharmacological therapy, cognitive behavioural therapies, invasive surgical procedures, endoscopic procedures and lifestyle alterations. Owing to the major differences between organ involvement, disease mechanisms and individual factors, treatment always needs to be personalized and some data suggest that phenotyping and subsequent individual management of GI pain might be options in the future.

Pathophysiology and management of diabetic gastroenteropathy

Polyneuropathy is a common complication to diabetes. Neuropathies within the enteric nervous system are associated with gastroenteropathy and marked symptoms that severely reduce quality of life. Symptoms are pleomorphic but include nausea, vomiting, dysphagia, dyspepsia, pain, bloating, diarrhoea, constipation and faecal incontinence. The aims of this review are fourfold. First, to provide a summary of the pathophysiology underlying diabetic gastroenteropathy. Secondly to give an overview of the diagnostic methods. Thirdly, to provide clinicians with a focussed overview of current and future methods for pharmacological and nonpharmacological treatment modalities. Pharmacological management is categorised according to symptoms arising from the upper or lower gut as well as sensory dysfunctions. Dietary management is central to improvement of symptoms and is discussed in detail, and neuromodulatory treatment modalities and other emerging management strategies for diabetic gastroenteropathy are discussed. Finally, we propose a diagnostic/investigation algorithm that can be used to support multidisciplinary management.

Physiologically Based Pharmacokinetic Modelling and Prediction of Metformin Pharmacokinetics in Renal/Hepatic-Impaired Young Adults and Elderly Populations

BACKGROUND AND OBJECTIVES

Physiologically based pharmacokinetic (PBPK) modelling and simulation enable researchers to overcome practical limitations for clinical trials on special populations. This study was conducted to investigate how the PBPK model describes the pharmacokinetics of metformin in young adult and elderly populations and to predict the pharmacokinetics of metformin in patients with renal or hepatic impairment in both populations.

METHODS

A first-order absorption/PBPK model for metformin was built in the Simcyp simulator version 14 release 1. A full PBPK model was constructed for metformin based on physicochemical properties and clinical observations. The model was refined and validated using clinical plasma concentration data obtained in healthy young adults and elderly after the oral administration of metformin. Metformin pharmacokinetics in patients with renal or hepatic impairment were then investigated and compared by simulation.

RESULTS

The PBPK model reasonably predicted the pharmacokinetic profiles of metformin for both young adults and the elderly. The predicted pharmacokinetic parameters, including maximum concentration, area under the time-concentration curve, and apparent oral clearance values, were within 1.5-fold of the observed data of metformin. In the simulation results, the systemic exposure of metformin was expected to be markedly increased not only with a decrease in renal function but also with severe hepatic impairments.

CONCLUSIONS

The PBPK model adequately characterised the pharmacokinetics of metformin in both young adult and elderly populations. PBPK modelling and simulation can be used as a useful tool to investigate and compare the pharmacokinetics in geriatric populations incorporating various disease conditions.

Sex-related pharmacokinetic differences and mechanisms of metapristone (RU486 metabolite)

Metapristone is the primary metabolite of the abortifacient mifepristone (RU486), and is being developed as a safe and effective cancer metastatic chemopreventive agent for both sexes. Here, we systematically investigated the sex-related pharmacokinetics of metapristone in both rats and dogs, and explored the related mechanisms of actions. Administration of metapristone to rats and dogs showed that plasma concentrations of metapristone (AUC, C _max ) were significantly higher in female dogs and rats than in males. The sex-related differences in pharmacokinetics become more significant after ten consecutive days of oral administration. Female liver microsomes metabolized metapristone significantly slower than the male ones. The results from P450 reaction phenotyping using recombinant cDNA-expressed human CYPs in conjunction with specific CYP inhibitors suggested that CYP1A2 and CYP3A4 are the predominant CYPs involved in the metapristone metabolism, which were further confirmed by the enhanced protein levels of CYP1A2 and CYP3A4 induced by 1-week oral administration of metapristone to rats. The highest tissue concentration of metapristone was found in the liver. The study demonstrates, for the first time, the sex-related pharmacokinetics of metapristone, and reveals that activities of liver microsomal CYP1A2 and CYP3A4 as well as the renal clearance are primarily responsible for the sex-related pharmacokinetics.

Prediction of cytochrome P450-mediated drug clearance in humans based on the measured activities of selected CYPs

Determining drug-metabolizing enzyme activities on an individual basis is an important component of personalized medicine, and cytochrome P450 enzymes (CYPs) play a principal role in hepatic drug metabolism. Herein, a simple method for predicting the major CYP-mediated drug clearance in vitro and in vivo is presented. Ten CYP-mediated drug metabolic activities in human liver microsomes (HLMs) from 105 normal liver samples were determined. The descriptive models for predicting the activities of these CYPs in HLMs were developed solely on the basis of the measured activities of a smaller number of more readily assayed CYPs. The descriptive models then were combined with the Conventional Bias Corrected in vitro–in vivo extrapolation method to extrapolate drug clearance in vivo. The V_max, K_m, and CL_int of six CYPs (CYP2A6, 2C8, 2D6, 2E1, and 3A4/5) could be predicted by measuring the activities of four CYPs (CYP1A2, 2B6, 2C9, and 2C19) in HLMs. Based on the predicted CL_int, the values of CYP2A6-, 2C8-, 2D6-, 2E1-, and 3A4/5-mediated drug clearance in vivo were extrapolated and found that the values for all five drugs were close to the observed clearance in vivo. The percentage of extrapolated values of clearance in vivo which fell within 2-fold of the observed clearance ranged from 75.2% to 98.1%. These findings suggest that measuring the activity of CYP1A2, 2B6, 2C9, and 2C19 allowed us to accurately predict CYP2A6-, 2C8-, 2D6-, 2E1-, and 3A4/5-mediated activities in vitro and in vivo and may possibly be helpful for the assessment of an individual’s drug metabolic profile.

Application of Physiologically Based Absorption Modeling to Characterize the Pharmacokinetic Profiles of Oral Extended Release Methylphenidate Products in Adults

A previously presented physiologically-based pharmacokinetic model for immediate release (IR) methylphenidate (MPH) was extended to characterize the pharmacokinetic behaviors of oral extended release (ER) MPH formulations in adults for the first time. Information on the anatomy and physiology of the gastrointestinal (GI) tract, together with the biopharmaceutical properties of MPH, was integrated into the original model, with model parameters representing hepatic metabolism and intestinal non-specific loss recalibrated against in vitro and in vivo kinetic data sets with IR MPH. A Weibull function was implemented to describe the dissolution of different ER formulations. A variety of mathematical functions can be utilized to account for the engineered release/dissolution technologies to achieve better model performance. The physiological absorption model tracked well the plasma concentration profiles in adults receiving a multilayer-release MPH formulation or Metadate CD, while some degree of discrepancy was observed between predicted and observed plasma concentration profiles for Ritalin LA and Medikinet Retard. A local sensitivity analysis demonstrated that model parameters associated with the GI tract significantly influenced model predicted plasma MPH concentrations, albeit to varying degrees, suggesting the importance of better understanding the GI tract physiology, along with the intestinal non-specific loss of MPH. The model provides a quantitative tool to predict the biphasic plasma time course data for ER MPH, helping elucidate factors responsible for the diverse plasma MPH concentration profiles following oral dosing of different ER formulations.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.