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

Impact of lactoferrin supplementation on cotrimoxazole pharmacokinetics: A preliminary clinical investigation

Background and purpose

Cotrimoxazole, a commonly prescribed antibiotic, has substantial resistance, especially in Indonesia, with its uropathogenic resistance reaching 67% in 2017. Although cotrimoxazole has been suggested to be co-administered with lactoferrin to enhance its antibacterial effectiveness and this practice has been widely adopted since the Covid-19 pandemic, the impact of lactoferrin on the pharmacokinetics of cotrimoxazole remains relatively unknown. This study aims to conduct a preliminary clinical investigation into the impact of lactoferrin supplementation on the pharmacokinetics of cotrimoxazole, focusing on the elimination rate and excretion of unchanged drug in urine.

Experimental approach

This study employed a blinded, cross-over, single-dose pharmacokinetics investigation, which included five healthy volunteers as participants. In the initial period, the first group received cotrimoxazole (80 mg trimethoprim and 400 mg sulfamethoxazole) along with a lactoferrin-containing supplement, while the second group only received cotrimoxazole. Subsequently, after a washout period, the conditions were reversed. Urine sampling was conducted at intervals from 0 to 24 hours post-medication, and drug levels in the urine were determined using high-performance liquid chromatography.

Key results

The population-based pharmacokinetic analysis revealed that the optimal model was the one-compartment model with first-order elimination and proportional residual error.

Conclusion

The findings show that the administration of lactoferrin-containing supplements did not significantly influence the covariate model and, therefore, did not alter the pharmacokinetics parameter of cotrimoxazole in urine with a single administration, implying that lactoferrin did not cause drug interaction problems when given simultaneously.

Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

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.

Predicted Environmental Risk Assessment of Antimicrobials with Increased Consumption in Portugal during the COVID-19 Pandemic; The Groundwork for the Forthcoming Water Quality Survey

The environmental release of antimicrobial pharmaceuticals is an imminent threat due to ecological impacts and microbial resistance phenomena. The recent COVID-19 outbreak will likely lead to greater loads of antimicrobials in the environment. Thus, identifying the most used antimicrobials likely to pose environmental risks would be valuable. For that, the ambulatory and hospital consumption patterns of antimicrobials in Portugal during the COVID-19 pandemic (2020–2021) were compared with those of 2019. A predicted risk assessment screening approach based on exposure and hazard in the surface water was conducted, combining consumption, excretion rates, and ecotoxicological/microbiological endpoints in five different regions of Portugal. Among the 22 selected substances, only rifaximin and atovaquone demonstrated predicted potential ecotoxicological risks for aquatic organisms. Flucloxacillin, piperacillin, tazobactam, meropenem, ceftriaxone, fosfomycin, and metronidazole showed the most significant potential for antibiotic resistance in all analysed regions. Regarding the current screening approach and the lack of environmental data, it is advisable to consider rifaximin and atovaquone in subsequent water quality surveys. These results might support the forthcoming monitorisation of surface water quality in a post-pandemic survey.

Developmental Pharmacokinetics of Antibiotics Used in Neonatal ICU: Focus on Preterm Infants

Neonatal Infections are among the most common reasons for admission to the intensive care unit. Neonatal sepsis (NS) significantly contributes to mortality rates. Empiric antibiotic therapy of NS recommended by current international guidelines includes benzylpenicillin, ampicillin/amoxicillin, and aminoglycosides (gentamicin). The rise of antibacterial resistance precipitates the growth of the use of antibiotics of the Watch (second, third, and fourth generations of cephalosporines, carbapenems, macrolides, glycopeptides, rifamycins, fluoroquinolones) and Reserve groups (fifth generation of cephalosporines, oxazolidinones, lipoglycopeptides, fosfomycin), which are associated with a less clinical experience and higher risks of toxic reactions. A proper dosing regimen is essential for effective and safe antibiotic therapy, but its choice in neonates is complicated with high variability in the maturation of organ systems affecting drug absorption, distribution, metabolism, and excretion. Changes in antibiotic pharmacokinetic parameters result in altered efficacy and safety. Population pharmacokinetics can help to prognosis outcomes of antibiotic therapy, but it should be considered that the neonatal population is heterogeneous, and this heterogeneity is mainly determined by gestational and postnatal age. Preterm neonates are common in clinical practice, and due to the different physiology compared to the full terms, constitute a specific neonatal subpopulation. The objective of this review is to summarize the evidence about the developmental changes (specific for preterm and full-term infants, separately) of pharmacokinetic parameters of antibiotics used in neonatal intensive care units.

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.

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.

Evaluation for Potential Drug-Drug Interaction of MT921 Using In Vitro Studies and Physiologically-Based Pharmacokinetic Models

MT921 is a new injectable drug developed by Medytox Inc. to reduce submental fat. Cholic acid is the active pharmaceutical ingredient, a primary bile acid biosynthesized from cholesterol, endogenously produced by liver in humans and other mammals. Although individuals treated with MT921 could be administered with multiple medications, such as those for hypertension, diabetes, and hyperlipidemia, the pharmacokinetic drug–drug interaction (DDI) has not been investigated yet. Therefore, we studied in vitro against drug-metabolizing enzymes and transporters. Moreover, we predicted the potential DDI between MT921 and drugs for chronic diseases using physiologically-based pharmacokinetic (PBPK) modeling and simulation. The magnitude of DDI was found to be negligible in in vitro inhibition and induction of cytochrome P450s and UDP-glucuronosyltransferases. Organic anion transporting polypeptide (OATP)1B3, organic anion transporter (OAT)3, Na⁺-taurocholate cotransporting polypeptide (NTCP), and apical sodium-dependent bile acid transporter (ASBT) are mainly involved in MT921 transport. Based on the result of in vitro experiments, the PBPK model of MT921 was developed and evaluated by clinical data. Furthermore, the PBPK model of amlodipine was developed and evaluated. PBPK DDI simulation results indicated that the pharmacokinetics of MT921 was not affected by the perpetrator drugs. In conclusion, MT921 could be administered without a DDI risk based on in vitro study and related in silico simulation. Further clinical studies are needed to validate this finding.