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

No dose adjustment of metformin or substrates of organic cation transporters (OCT)1 and OCT2 and multidrug and toxin extrusion protein (MATE)1/2K with fostemsavir coadministration based on modeling approaches

Fostemsavir is an approved gp120-directed attachment inhibitor and prodrug for the treatment of human immunodeficiency virus type 1 infection in combination with other antiretrovirals (ARVs) in heavily treatment-experienced adults with multi-drug resistance, intolerance, or safety concerns with their current ARV regimen. Initial in vitro studies indicated that temsavir, the active moiety of fostemsavir, and its metabolites, inhibited organic cation transporter (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs) at tested concentration of 100 uM, although risk assessment based on the current Food and Drug Administration in vitro drug-drug interaction (DDI) guidance using the mechanistic static model did not reveal any clinically relevant inhibition on OCTs and MATEs. However, a DDI risk was flagged with EMA static model predictions. Hence, a physiologically based pharmacokinetic (PBPK) model of fostemsavir/temsavir was developed to further assess the DDI risk potential of OCT and MATEs inhibition by temsavir and predict changes in metformin (a sensitive OCT and MATEs substrate) exposure. No clinically relevant impact on metformin concentrations across a wide range of temsavir concentrations was predicted; therefore, no dose adjustment is recommended for metformin when co-administered with fostemsavir.

Pharmacological characterization of the antidiabetic drug metformin in atherosclerosis inhibition: A comprehensive insight

BACKGROUND

Atherosclerosis (AS) is a progressive disease that interferes with blood flow, leading to cardiovascular complications such as hypertension, ischemic heart disease, ischemic stroke, and vascular ischemia. The progression of AS is correlated with inflammation, oxidative stress, and endothelial dysfunction. Various signaling pathways, like nuclear erythroid-related factor 2 (Nrf2) and Kruppel-like factor 2 (KLF2), are involved in the pathogenesis of AS. Nrf2 and KLF2 have anti-inflammatory and antioxidant properties. Thus, activation of these pathways may reduce the development of AS. Metformin, an insulin-sensitizing drug used in the management of type 2 diabetes mellitus (T2DM), increases the expression of Nrf2 and KLF2. AS is a common long-term macrovascular complication of T2DM. Thus, metformin, through its pleiotropic anti-inflammatory effect, may attenuate the development and progression of AS.

AIMS

Therefore, this review aims to investigate the possible role of metformin in AS concerning its effect on Nrf2 and KLF2 and inhibition of reactive oxygen species (ROS) formation. In addition to its antidiabetic effect, metformin can reduce cardiovascular morbidities and mortalities compared to other antidiabetic agents, even with similar blood glucose control by the Nrf2/KLF2 pathway activation.

CONCLUSION

In conclusion, metformin is an effective therapeutic strategy against the development and progression of AS, mainly through activation of the KLF2/Nrf2 axis.

Metformin role in Parkinson's disease: a double-sword effect

Parkinson's disease (PD) is a common neurodegenerative disease developed due to the degeneration of dopaminergic neurons in the substantia nigra. There is no single effective treatment in the management of PD. Therefore, repurposing effective and approved drugs like metformin could be an effective strategy for managing PD. However, the mechanistic role of metformin in PD neuropathology was not fully elucidated. Metformin is an insulin-sensitizing agent used as a first-line therapy in the management of type 2 diabetes mellitus (T2DM) and has the ability to reduce insulin resistance (IR). Metformin may have a beneficial effect on PD neuropathology. The neuroprotective effect of metformin is mainly mediated by activating adenosine monophosphate protein kinase (AMPK), which reduces mitochondrial dysfunction, oxidative stress, and α-synuclein aggregation. As well, metformin mitigates brain IR a hallmark of PD and other neurodegenerative diseases. However, metformin may harm PD neuropathology by inducing hyperhomocysteinemia and deficiency of folate and B12. Therefore, this review aimed to find the potential role of metformin regarding its protective and detrimental effects on the pathogenesis of PD. The mechanistic role of metformin in PD neuropathology was not fully elucidated. Most studies regarding metformin and its effectiveness in PD neuropathology were observed in preclinical studies, which are not fully translated into clinical settings. In addition, metformin effect on PD neuropathology was previously clarified in T2DM, potentially linked to an increasing PD risk. These limitations hinder the conclusion concerning the therapeutic efficacy of metformin and its beneficial and detrimental role in PD. Therefore, as metformin does not cause hypoglycemia and is a safe drug, it should be evaluated in non-diabetic patients concerning PD risk.

[Novel equations for estimating renal function: significance for drug dose adjustment]

BACKGROUND

Important therapeutic decisions depend on kidney function, which is why its correct assessment is of great importance. It also plays an important role for drug dose adjustments in patients with impaired kidney function.

OBJECTIVES

In clinical practice, kidney function is almost always estimated using mathematical glomerular filtration rate (GFR) equations. To estimate GFR, the patient's age and gender as well as kidney-specific endogenous biomarkers are required. This work aims to provide an overview of the advantages and disadvantages of the biomarkers serum creatinine and cystatin C in assessing kidney function. Particularly in patients with significantly reduced or increased muscle mass, creatinine is not suitable for determining GFR, and cystatin C should be used. Currently recommended GFR estimating equations are described, illustrating for which patient groups they can be used.

CURRENT DATA

A large number of high-ranking publications are available investigating the validity of GFR estimating equations and the optimal choice of endogenous biomarkers. However, there are still large gaps when it comes to drug approval studies in older patients and children.

CONCLUSION

Estimated GFR (eGFR) is only a rough estimate of kidney function and should not be interpreted as an exact number. Drug dose adjustments may be necessary in patients with an eGFR of < 50 ml/min and should be verified particularly in severely impaired GFR (< 30 ml/min). There are tools available online for this purpose.

Metformin-Cimetidine Drug Interaction and Risk of Lactic Acidosis in Renal Failure: A Pharmacovigilance-Pharmacokinetic Appraisal

OBJECTIVE

This study aimed to evaluate lactic acidosis (LA) risk when using metformin combined with histamine H2 receptor inhibitors (H2RI) in patients with renal failure (RF).

RESEARCH DESIGN AND METHODS

This study analyzed FDA Adverse Event Reporting System data (2012Q4 to 2022Q4) to characterize reports of LA associated with metformin alone or combined with H2RI. Using a disproportionality approach, LA risk signal in the overall population and in patients with RF was assessed. A physiologically based pharmacokinetic (PBPK) model was developed to predict metformin and cimetidine pharmacokinetic changes following conventional doses of the combinations in patients with various degrees of RF. To explore its correlation with LA risk, a peak plasma metformin concentration of 3 mg/L was considered the threshold.

RESULTS

Following the 2016 U.S. Food and Drug Administration metformin approval for mild-to-moderate RF, the percentage of patients with RF reporting LA associated with metformin combined with H2RI increased. Disproportionality analysis showed reported LA risk signal associated with metformin and cimetidine in the overall population within the study timeframe only. Furthermore, with PBPK simulations, for metformin (1,000 mg b.i.d.) with cimetidine (300 mg q.i.d. or 400 mg b.i.d.) in stage 1 of chronic kidney disease, metformin (1,000 mg b.i.d.) with cimetidine (300 mg q.i.d. or 400 mg b.i.d. or 800 mg q.d.) in stage 2, and most combinations in stage 3, the peak plasma metformin concentrations exceeded the 3 mg/L threshold.

CONCLUSIONS

Metformin combined with cimetidine at conventional doses may cause LA in patients with mild-to-moderate RF.

Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

Significant impact of time-of-day variation on metformin pharmacokinetics

AIMS/HYPOTHESIS

The objective was to investigate if metformin pharmacokinetics is modulated by time-of-day in humans using empirical and mechanistic pharmacokinetic modelling techniques on a large clinical dataset. This study also aimed to generate and test hypotheses on the underlying mechanisms, including evidence for chronotype-dependent interindividual differences in metformin plasma and efficacy-related tissue concentrations.

METHODS

A large clinical dataset consisting of individual metformin plasma and urine measurements was analysed using a newly developed empirical pharmacokinetic model. Causes of daily variation of metformin pharmacokinetics and interindividual variability were further investigated by a literature-informed mechanistic modelling analysis.

RESULTS

A significant effect of time-of-day on metformin pharmacokinetics was found. Daily rhythms of gastrointestinal, hepatic and renal processes are described in the literature, possibly affecting drug pharmacokinetics. Observed metformin plasma levels were best described by a combination of a rhythm in GFR, renal plasma flow (RPF) and organic cation transporter (OCT) 2 activity. Furthermore, the large interindividual differences in measured metformin concentrations were best explained by individual chronotypes affecting metformin clearance, with impact on plasma and tissue concentrations that may have implications for metformin efficacy.

CONCLUSIONS/INTERPRETATION

Metformin's pharmacology significantly depends on time-of-day in humans, determined with the help of empirical and mechanistic pharmacokinetic modelling, and rhythmic GFR, RPF and OCT2 were found to govern intraday variation. Interindividual variation was found to be partly dependent on individual chronotype, suggesting diurnal preference as an interesting, but so-far underappreciated, topic with regard to future personalised chronomodulated therapy in people with type 2 diabetes.

Generic Workflow to Predict Medicine Concentrations in Human Milk Using Physiologically-Based Pharmacokinetic (PBPK) Modelling-A Contribution from the ConcePTION Project

Women commonly take medication during lactation. Currently, there is little information about the exposure-related safety of maternal medicines for breastfed infants. The aim was to explore the performance of a generic physiologically-based pharmacokinetic (PBPK) model to predict concentrations in human milk for ten physiochemically diverse medicines. First, PBPK models were developed for "non-lactating" adult individuals in PK-Sim/MoBi v9.1 (Open Systems Pharmacology). The PBPK models predicted the area-under-the-curve (AUC) and maximum concentrations (C_max) in plasma within a two-fold error. Next, the PBPK models were extended to include lactation physiology. Plasma and human milk concentrations were simulated for a three-months postpartum population, and the corresponding AUC-based milk-to-plasma (M/P) ratios and relative infant doses were calculated. The lactation PBPK models resulted in reasonable predictions for eight medicines, while an overprediction of human milk concentrations and M/P ratios (>2-fold) was observed for two medicines. From a safety perspective, none of the models resulted in underpredictions of observed human milk concentrations. The present effort resulted in a generic workflow to predict medicine concentrations in human milk. This generic PBPK model represents an important step towards an evidence-based safety assessment of maternal medication during lactation, applicable in an early drug development stage.

Physiologically based pharmacokinetic-pharmacodynamic evaluation of meropenem in CKD and hemodialysis individuals

Objective: Chronic kidney disease (CKD) has significant effects on renal clearance of drugs. The application of antibiotics in CKD patients to achieve the desired therapeutic effect is challenging. This study aims to determine meropenem plasma exposure in the CKD population and further investigate optimal dosing regimens. Methods: A healthy adult PBPK model was established using the meropenem's physicochemical parameters, pharmacokinetic parameters, and available clinical data, and it was scaled to the populations with CKD and dialysis. The differences between the predicted concentration, C_max, and AUC_last predicted and observed model values were assessed by mean relative deviations (MRD) and geometric mean fold errors (GMFE) values and plotting the goodness of fit plot to evaluate the model's performance. Finally, dose recommendations for CKD and hemodialysis populations were performed by Monte Carlo simulations. Results: The PBPK models of meropenem in healthy, CKD, and hemodialysis populations were successfully established. The MRD values of the predicted concentration and the GMFE values of C_max and AUC_last were within 0.5-2.0-fold of the observed data. The simulation results of the PBPK model showed the increase in meropenem exposure with declining kidney function in CKD populations. The dosing regimen of meropenem needs to be further adjusted according to the renal function of CKD patients. In patients receiving hemodialysis, since meropenem declined more rapidly during the on-dialysis session than the off-dialysis session, pharmacodynamic evaluations were performed for two periods separately, and respective optimal dosing regimens were determined. Conclusion: The established PBPK model successfully predicted meropenem pharmacokinetics in patients with CKD and hemodialysis and could further be used to optimize dosing recommendations, providing a reference for personalized clinical medication.

An Analytical Solution for Saturable Absorption in Pharmacokinetics Models

OBJECTIVE

The first-order absorption is a common model used in Pharmacokinetics. The absorption of some drugs follows carrier mediated transport. It has been proposed that the amount of drug available may saturate the transport mechanism resulting in an absorption slower than the one predicted by the first-order model. Saturable absorption has been modeled at the differential equation level by substituting the constant rate absorption by a Hill kinetics absorption. However, its exact solution is so far unknown. The goal of this is to know the exact solution of different Hill kinetic absorption models.

METHODS

We start defining different absorption models and increasing then their complexity. The simplest case is the first-order absorption model and the most complex will be a generalized Hill kinetic absorption model. The differential equation of each model is integrated.

RESULTS

The complexity of the models their solutions may be not expressed in a close-form, or in term of elementary functions. We obtain and discuss the exact solutions of the different Hill kinetics absorption models. To do that, the solutions are studied according to the possible values of the free parameters of the models. We show the differences between models through simulations.

CONCLUSIONS

The knowledge of closed-form solutions allows to illustrate the differences between the different absorption models and minimizes the errors of numerical integration.

Renal Transporter-Mediated Drug-Biomarker Interactions of the Endogenous Substrates Creatinine and N1 -Methylnicotinamide: A PBPK Modeling Approach

Endogenous biomarkers for transporter-mediated drug-drug interaction (DDI) predictions represent a promising approach to facilitate and improve conventional DDI investigations in clinical studies. This approach requires high sensitivity and specificity of biomarkers for the targets of interest (e.g., transport proteins), as well as rigorous characterization of their kinetics, which can be accomplished utilizing physiologically-based pharmacokinetic (PBPK) modeling. Therefore, the objective of this study was to develop PBPK models of the endogenous organic cation transporter (OCT)2 and multidrug and toxin extrusion protein (MATE)1 substrates creatinine and N¹ -methylnicotinamide (NMN). Additionally, this study aimed to predict kinetic changes of the biomarkers during administration of the OCT2 and MATE1 perpetrator drugs trimethoprim, pyrimethamine, and cimetidine. Whole-body PBPK models of creatinine and NMN were developed utilizing studies investigating creatinine or NMN exogenous administration and endogenous synthesis. The newly developed models accurately describe and predict observed plasma concentration-time profiles and urinary excretion of both biomarkers. Subsequently, models were coupled to the previously built and evaluated perpetrator models of trimethoprim, pyrimethamine, and cimetidine for interaction predictions. Increased creatinine plasma concentrations and decreased urinary excretion during the drug-biomarker interactions with trimethoprim, pyrimethamine, and cimetidine were well-described. An additional inhibition of NMN synthesis by trimethoprim and pyrimethamine was hypothesized, improving NMN plasma and urine interaction predictions. To summarize, whole-body PBPK models of creatinine and NMN were built and evaluated to better assess creatinine and NMN kinetics while uncovering knowledge gaps for future research. The models can support investigations of renal transporter-mediated DDIs during drug development.

Theophylline: Old Drug in a New Light, Application in COVID-19 through Computational Studies

Theophylline (3-methyxanthine) is a historically prominent drug used to treat respiratory diseases, alone or in combination with other drugs. The rapid onset of the COVID-19 pandemic urged the development of effective pharmacological treatments to directly attack the development of new variants of the SARS-CoV-2 virus and possess a therapeutical battery of compounds that could improve the current management of the disease worldwide. In this context, theophylline, through bronchodilatory, immunomodulatory, and potentially antiviral mechanisms, is an interesting proposal as an adjuvant in the treatment of COVID-19 patients. Nevertheless, it is essential to understand how this compound could behave against such a disease, not only at a pharmacodynamic but also at a pharmacokinetic level. In this sense, the quickest approach in drug discovery is through different computational methods, either from network pharmacology or from quantitative systems pharmacology approaches. In the present review, we explore the possibility of using theophylline in the treatment of COVID-19 patients since it seems to be a relevant candidate by aiming at several immunological targets involved in the pathophysiology of the disease. Theophylline down-regulates the inflammatory processes activated by SARS-CoV-2 through various mechanisms, and herein, they are discussed by reviewing computational simulation studies and their different applications and effects.

Physiologically-based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups

This study provides a whole‐body physiologically‐based pharmacokinetic (PBPK) model of dextromethorphan and its metabolites dextrorphan and dextrorphan O‐glucuronide for predicting the effects of cytochrome P450 2D6 (CYP2D6) drug‐gene interactions (DGIs) on dextromethorphan pharmacokinetics (PK). Moreover, the effect of interindividual variability (IIV) within CYP2D6 activity score groups on the PK of dextromethorphan and its metabolites was investigated. A parent‐metabolite‐metabolite PBPK model of dextromethorphan, dextrorphan, and dextrorphan O‐glucuronide was developed in PK‐Sim and MoBi. Drug‐dependent parameters were obtained from the literature or optimized. Plasma concentration‐time profiles of all three analytes were gathered from published studies and used for model development and model evaluation. The model was evaluated comparing simulated plasma concentration‐time profiles, area under the concentration‐time curve from the time of the first measurement to the time of the last measurement (AUC_last) and maximum concentration (C_max) values to observed study data. The final PBPK model accurately describes 28 population plasma concentration‐time profiles and plasma concentration‐time profiles of 72 individuals from four cocktail studies. Moreover, the model predicts CYP2D6 DGI scenarios with six of seven DGI AUC_last and seven of seven DGI C_max ratios within the acceptance criteria. The high IIV in plasma concentrations was analyzed by characterizing the distribution of individually optimized CYP2D6 k_cat values stratified by activity score group. Population simulations with sampling from the resulting distributions with calculated log‐normal dispersion and mean parameters could explain a large extent of the observed IIV. The model is publicly available alongside comprehensive documentation of model building and model evaluation.

Physiologically Based Pharmacokinetic Modeling of Metformin in Children and Adolescents with Obesity

Childhood obesity continues to rise in the United States, and with it the off-label use of metformin for weight loss. The influence of age and obesity on the drug's disposition and exposure has not previously been studied using a mechanistic framework. Here, an adult physiologically based pharmacokinetic (PBPK) model of metformin was scaled to pediatric populations without obesity, with overweight / obesity, and with severe obesity; a published virtual population of children and adolescents with obesity was leveraged during model evaluation. When the pediatric model was simulated in groups 10 - 18 y of age, oral clearance (CL/F) following 1,000 mg of metformin was higher (∼1200 mL/min) in those with obesity and severe obesity compared to the groups without and with overweight (∼1000 mL/min). In addition, simulated AUC in older children and adolescents with obesity and severe obesity was comparable to that in adults with a similar dose-exposure relationship. Overall, simulations using the pediatric PBPK model support the use of adult doses of metformin in older children and adolescents with obesity. Moreover, the virtual population of children and adolescents with obesity offers a valuable tool to facilitate development of pediatric PBPK models for studying populations with obesity and, in turn, contribute information to inform drug labeling in this special population. This article is protected by copyright. All rights reserved.

PBPK Simulation-Based Evaluation of Ganciclovir Crystalluria Risk Factors: Effect of Renal Impairment, Old Age, and Low Fluid Intake

Dosing guidance is often lacking for chronic kidney disease (CKD) due to exclusion of such patients from pivotal clinical trials. Physiologically based pharmacokinetic (PBPK) modelling supports model-informed dosing when clinical data are lacking, but application of these approaches to patients with impaired renal function is not yet at full maturity. In the current study, a ganciclovir PBPK model was developed for patients with normal renal function and extended to CKD population. CKD-related changes in tubular secretion were explored in the mechanistic kidney model and implemented either as proportional or non-proportional decline relative to GFR. Crystalluria risk was evaluated in different clinical settings (old age, severe CKD and low fluid intake) by simulating ganciclovir medullary collecting duct (MCD) concentrations. The ganciclovir PBPK model captured observed changes in systemic pharmacokinetic endpoints in mild-to-severe CKD; these trends were evident irrespective of assumed pathophysiological mechanism of altered active tubular secretion in the model. Minimal difference in simulated ganciclovir MCD concentrations was noted between young adult and geriatric populations with normal renal function and urine flow (1 mL/min), with lower concentrations predicted for severe CKD patients. High crystalluria risk was identified at reduced urine flow (0.1 mL/min) as simulated ganciclovir MCD concentrations exceeded its solubility (2.6–6 mg/mL), irrespective of underlying renal function. The analysis highlighted the importance of appropriate distribution of virtual subjects’ systems data in CKD populations. The ganciclovir PBPK model illustrates the ability of this translational tool to explore individual and combined effects of age, urine flow, and renal impairment on local drug renal exposure.

Host-directed therapies for tuberculosis: quantitative systems pharmacology approaches

Host-directed therapies (HDTs) that modulate host-pathogen interactions offer an innovative strategy to combat Mycobacterium tuberculosis (Mtb) infections. When combined with tuberculosis (TB) antibiotics, HDTs could contribute to improving treatment outcomes, reducing treatment duration, and preventing resistance development. Translation of the interplay of host-pathogen interactions leveraged by HDTs towards therapeutic outcomes in patients is challenging. Quantitative understanding of the multifaceted nature of the host-pathogen interactions is vital to rationally design HDT strategies. Here, we (i) provide an overview of key Mtb host-pathogen interactions as basis for HDT strategies; and (ii) discuss the components and utility of quantitative systems pharmacology (QSP) models to inform HDT strategies. QSP models can be used to identify and optimize treatment targets, to facilitate preclinical to human translation, and to design combination treatment strategies.

Integrated or Independent Actions of Metformin in Target Tissues Underlying Its Current Use and New Possible Applications in the Endocrine and Metabolic Disorder Area

Metformin is considered the first-choice drug for type 2 diabetes treatment. Actually, pleiotropic effects of metformin have been recognized, and there is evidence that this drug may have a favorable impact on health beyond its glucose-lowering activity. In summary, despite its long history, metformin is still an attractive research opportunity in the field of endocrine and metabolic diseases, age-related diseases, and cancer. To this end, its mode of action in distinct cell types is still in dispute. The aim of this work was to review the current knowledge and recent findings on the molecular mechanisms underlying the pharmacological effects of metformin in the field of metabolic and endocrine pathologies, including some endocrine tumors. Metformin is believed to act through multiple pathways that can be interconnected or work independently. Moreover, metformin effects on target tissues may be either direct or indirect, which means secondary to the actions on other tissues and consequent alterations at systemic level. Finally, as to the direct actions of metformin at cellular level, the intracellular milieu cooperates to cause differential responses to the drug between distinct cell types, despite the primary molecular targets may be the same within cells. Cellular bioenergetics can be regarded as the primary target of metformin action. Metformin can perturb the cytosolic and mitochondrial NAD/NADH ratio and the ATP/AMP ratio within cells, thus affecting enzymatic activities and metabolic and signaling pathways which depend on redox- and energy balance. In this context, the possible link between pyruvate metabolism and metformin actions is extensively discussed.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions

Purpose

To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs).

Methods

The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model.

Results

The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUC_last ratios (AUC_last during DDI/AUC_last without co-administration) and DDI C_max ratios (C_max during DDI/C_max without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values.

Conclusions

A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-021-03109-6.

Metformin Transport Rates Between Plasma and Red Blood Cells in Humans

Background

Metformin has been used for the treatment of type 2 diabetes for over 60 years; however, its mechanism of pharmacological action is not fully clear. Different hypotheses exist regarding metformin distribution and redistribution mechanisms between plasma and erythrocytes/red blood cells (RBCs).

Objective

We aimed to test the hypothesis that the metformin distribution between plasma and RBC occurs via concentration difference-driven passive transport and estimated transport rate coefficient values based on metformin concentration time series in plasma and RBCs from in vivo studies.

Methods

An ordinary differential equation (ODE) system with two compartments was used to describe diffusion-based passive transport between plasma and RBCs. Metformin concentration time series in plasma and RBCs of 35 individuals were used for metformin transport parametrization. Plasma concentration has been approximated by biexponential decline.

Results

A single passive transport coefficient, k = 0.044 ± 0.014 (h^–1), can be applied, describing the uptake and release transport rate versus the linear equation v = k × (M_pl − M_RBC), where M_pl is the metformin concentration in plasma and M_RBC is the metformin concentration in RBCs.

Conclusions

Our research suggests that passive transport can explain metformin distribution dynamics between plasma and RBCs because transport speed is proportional to the metformin concentration difference and independent of the transport direction. Concentration difference-driven passive transport can explain the mechanism of faster metformin distribution to RBCs the first few hours after administration, and faster release and domination of the redistribution transport rate after metformin concentration in plasma becomes smaller than in RBCs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-021-01058-2.

Meta-Assessment of Metformin Absorption and Disposition Pharmacokinetics in Nine Species

The objective of this study was to systematically assess literature datasets and quantitatively analyze metformin PK in plasma and some tissues of nine species. The pharmacokinetic (PK) parameters and profiles of metformin in nine species were collected from the literature. Based on a simple allometric scaling, the systemic clearances (CL) of metformin in these species highly correlate with body weight (BW) (R2 = 0.85) and are comparable to renal plasma flow in most species except for rabbit and cat. Reported volumes of distribution (VSS) varied appreciably (0.32 to 10.1 L/kg) among species. Using the physiological and anatomical variables for each species, a minimal physiologically based pharmacokinetic (mPBPK) model consisting of blood and two tissue compartments (Tissues 1 and 2) was used for modeling metformin PK in the nine species. Permeability-limited distribution (low fd1 and fd2) and a single tissue-to-plasma partition coefficient (Kp) value for Tissues 1 and 2 were applied in the joint mPBPK fitting. Nonlinear regression analysis for common tissue distribution parameters along with species-specific CL values reasonably captured the plasma PK profiles of metformin across most species, except for rat and horse with later time deviations. In separate fittings of the mPBPK model to each species, Tissue 2 was considered as slowly-equilibrating compartment consisting of muscle and skin based on in silico calculations of the mean transit times through tissues. The well-fitted mPBPK model parameters for absorption and disposition PK of metformin for each species were compared with in vitro/in vivo results found in the literature with regard to the physiological details and physicochemical properties of metformin. Bioavailability and absorption rates decreased with the increased BW among the species. Tissues such as muscle dominate metformin distribution with low permeability and partitioning while actual tissue concentrations found in rats and mice show likely transporter-mediated uptake in liver, kidney, and gastrointestinal tissues. Metformin has diverse pharmacologic actions, and this assessment revealed allometric relationships in its absorption and renal clearance but considerable variability in actual and modeled tissue distribution probably caused by transporter differences.

Effect of Omecamtiv Mecarbil on the Pharmacokinetics of Metformin, a Probe Substrate for MATE1/MATE2-K, in Healthy Subjects

BACKGROUND AND OBJECTIVE

Omecamtiv mecarbil (OM) is a novel cardiac myosin activator in development for the treatment of heart failure with reduced ejection fraction. The objective of this study was to evaluate the potential for OM to affect the pharmacokinetics of metformin.

METHODS

This was an open-label, fixed-sequence study in 14 healthy subjects. On Day 1, subjects received an 850 mg oral dose of metformin. From Days 4 to 9, subjects received twice-daily 25 mg oral doses of OM tablets. On Day 10, subjects received an 850 mg oral dose of metformin and a single 25 mg tablet of OM. Blood and urine samples were collected up to 36 h post-dose following administration of metformin on Days 1 and 10 to characterize concentrations of metformin in plasma and urine.

RESULTS

The ratios of the geometric least square means of metformin coadministered with OM compared to metformin alone were 98.7%, 99.3%, and 110.2% for AUC_inf, AUC_last, and C_max, respectively. The mean renal clearance of metformin was similar following metformin administered alone (34.2 L/h) compared to metformin coadministered with OM (32.9 L/h). All adverse events were mild in severity and resolved prior to the end of the study. No serious adverse events or treatment-emergent adverse events led to discontinuation from the study.

CONCLUSIONS

There was no clinically relevant effect of OM on the pharmacokinetics of metformin in healthy subjects.

A generic framework for the physiologically-based pharmacokinetic platform qualification of PK-Sim and its application to predicting cytochrome P450 3A4-mediated drug-drug interactions

The success of applications of physiologically‐based pharmacokinetic (PBPK) modeling in drug development and drug labeling has triggered regulatory agencies to demand rigorous demonstration of the predictive capability of the specific PBPK platform for a particular intended application purpose. The effort needed to comply with such qualification requirements exceeds the costs for any individual PBPK application. Because changes or updates of a PBPK platform would require (re‐)qualification, a reliable and efficient generic qualification framework is needed. We describe the development and implementation of an agile and sustainable technical framework for automatic PBPK platform (re‐)qualification of PK‐Sim^® embedded in the open source and open science GitHub landscape of Open Systems Pharmacology. The qualification approach enables the efficient assessment of all aspects relevant to the qualification of a particular purpose and provides transparency and traceability for all stakeholders. As a showcase example for the power and versatility of the qualification framework, we present the qualification of PK‐Sim^® for the intended purpose of predicting cytochrome P450 3A4 (CYP3A4)–mediated drug–drug interactions (DDIs). Several perpetrator PBPK models featuring various degrees of CYP3A4 modulation and different types of mechanisms (competitive inhibition, mechanism‐based inactivation, and induction) were coupled with a set of PBPK models of sensitive CYP3A4 victim drugs. Simulations were compared to a comprehensive data set of 135 observations from published clinical DDI studies. The platform's overall predictive performance showed reasonable accuracy and precision (geometric mean fold error of 1.4 for both area under the plasma concentration‐time curve ratios and peak plasma concentration ratios with/without perpetrator) and suggests that PK‐Sim^® can be applied to quantitatively assess CYP3A4‐mediated DDI in clinically untested scenarios.

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.

Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases

The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio(obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).

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.

A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug-Drug-Gene Interaction Predictions

Trimethoprim is a frequently-prescribed antibiotic and therefore likely to be co-administered with other medications, but it is also a potent inhibitor of multidrug and toxin extrusion protein (MATE) and a weak inhibitor of cytochrome P450 (CYP) 2C8. The aim of this work was to develop a physiologically-based pharmacokinetic (PBPK) model of trimethoprim to investigate and predict its drug–drug interactions (DDIs). The model was developed in PK-Sim^®, using a large number of clinical studies (66 plasma concentration–time profiles with 36 corresponding fractions excreted in urine) to describe the trimethoprim pharmacokinetics over the entire published dosing range (40 to 960 mg). The key features of the model include intestinal efflux via P-glycoprotein (P-gp), metabolism by CYP3A4, an unspecific hepatic clearance process, and a renal clearance consisting of glomerular filtration and tubular secretion. The DDI performance of this new model was demonstrated by prediction of DDIs and drug–drug–gene interactions (DDGIs) of trimethoprim with metformin, repaglinide, pioglitazone, and rifampicin, with all predicted DDI and DDGI AUC_last and C_max ratios within 1.5-fold of the clinically-observed values. The model will be freely available in the Open Systems Pharmacology model repository, to support DDI studies during drug development.

A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug-Drug Interaction Studies

The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug–drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim^®, using 45 clinical studies (dosing range 0.1–250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or C_trough ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development.