Effects of Beta Lactams on Behavioral Outcomes of Substance Use Disorders: A Meta-Analysis of Preclinical Studies.

INTRODUCTION: Preclinical studies demonstrated that beta-lactams have neuroprotective effects in conditions involving glutamate neuroexcitotoxicity, including substance use disorders (SUDs). This meta-analysis aims to analyze the existing evidences on the effects of beta-lactams as glutamate transporter 1 (GLT-1) upregulators in animal models of SUDs, identification of gaps in the literature, and setting the stage for potential translation into clinical phases. METHODS: Meta-analysis was conducted on preclinical studies retrieved systematically from MEDLINE and ScienceDirect databases. Abused substances were identified by refereeing to the National Institute on Drug Abuse (NIDA). The results were quantitatively described with a focus on the behavioral outcomes. Treatment effect sizes were described using standardized mean difference, and they were pooled using random effect model. I2-statistic was used to assess heterogeneity, and Funnel plot and Egger's test were used for assessment of publication bias. RESULTS: Literature search yielded a total of 71 studies that were eligible to be included in the analysis. Through these studies, the effects of beta-lactams were evaluated in animal models of nicotine, cannabis, amphetamines, synthetic cathinone, opioids, ethanol, and cocaine use disorders as well as steroids-related aggressive behaviors. Meta-analysis showed that treatments with beta-lactams consistently reduced the pooled undesired effects of the abused substances in several paradigms, including drug-self administration, conditioned place preference, drug seeking behaviors, hyperlocomotion, withdrawal syndromes, tolerance to analgesic effects, hyperalgesia, and hyperthermia. CONCLUSION: This meta-analysis revealed that enhancing GLT-1 expression in the brain through beta-lactams seemed to be a promising treatment approach in the context of substance use disorders, as indicated by results in animal models.

A Physiologically Based Pharmacokinetic Model to Predict Systemic Ondansetron Concentration in Liver Cirrhosis Patients.

INTRODUCTION: Ondansetron is a drug that is routinely prescribed for the management of nausea and vomiting associated with cancer, radiation therapy, and surgical operations. It is mainly metabolized in the liver, and it might accumulate in patients with hepatic impairment and lead to unwanted adverse events. METHODS: A physiologically based pharmacokinetic (PBPK) model was developed to predict the exposure of ondansetron in healthy and liver cirrhosis populations. The population-based PBPK simulator PK-Sim was utilized for simulating ondansetron exposure in healthy and liver cirrhosis populations. RESULTS: The developed model successfully described the pharmacokinetics of ondansetron in healthy and liver cirrhosis populations. The predicted area under the curve, maximum systemic concentration, and clearance were within the allowed twofold range. The exposure of ondansetron in the population of Child-Pugh class C has doubled in comparison to Child-Pugh class A. The dose has to be adjusted for liver cirrhosis patients to ensure comparable exposure to a healthy population. CONCLUSION: In this study, the developed PBPK model has described the pharmacokinetics of ondansetron successfully. The PBPK model has been successfully evaluated to be used as a tool for dose adjustments in liver cirrhosis patients.

Liver Metabolomics and Inflammatory Profiles in Mouse Model of Fentanyl Overdose Treated with Beta-Lactams.

Fentanyl is a highly potent opioid analgesic that is approved medically to treat acute and chronic pain. There is a high potential for overdose-induced organ toxicities, including liver toxicity, and this might be due to the increase of recreational use of opioids. Several preclinical studies have demonstrated the efficacy of beta-lactams in modulating the expression of glutamate transporter-1 (GLT-1) in different body organs, including the liver. The upregulation of GLT-1 by beta-lactams is associated with the attenuation of hyperglutamatergic state, which is a characteristic feature of opioid use disorders. A novel experimental beta-lactam compound with no antimicrobial properties, MC-100093, has been developed to attenuate dysregulation of glutamate transport, in part by normalizing GLT-1 expression. A previous study showed that MC-100093 modulated hepatic GLT-1 expression with subsequent attenuation of alcohol-increased fat droplet content in the liver. In this study, we investigated the effects of fentanyl overdose on liver metabolites, and determined the effects of MC-100093 and ceftriaxone in the liver of a fentanyl overdose mouse model. Liver samples from control, fentanyl overdose, and fentanyl overdose ceftriaxone- or MC-100093-treated mice were analyzed for metabolomics using gas chromatography-mass spectrometry. Heatmap analysis revealed that both MC-100093 and ceftriaxone attenuated the effects of fentanyl overdose on several metabolites, and MC-100093 showed superior effects. Statistical analysis showed that MC-100093 reversed the effects of fentanyl overdose in some metabolites. Moreover, enrichment analysis revealed that the altered metabolites were strongly linked to the glucose-alanine cycle, the Warburg effect, gluconeogenesis, glutamate metabolism, lactose degradation, and ketone body metabolism. The changes in liver metabolites induced by fentanyl overdose were associated with liver inflammation, an effect attenuated with ceftriaxone pre-treatments. Ceftriaxone normalized fentanyl-overdose-induced changes in liver interleukin-6 and cytochrome CYP3A11 (mouse homolog of human CYP3A4) expression. Our data indicate that fentanyl overdose impaired liver metabolites, and MC-100093 restored certain metabolites.

Physiologically-based pharmacokinetic modeling for single and multiple dosing regimens of ceftriaxone in healthy and chronic kidney disease populations: a tool for model-informed precision dosing.

Introduction: Ceftriaxone is one of commonly prescribed beta-lactam antibiotics with several label and off-label clinical indications. A high fraction of administered dose of ceftriaxone is excreted renally in an unchanged form, and it may accumulate significantly in patients with impaired renal functions, which may lead to toxicity. Methods: In this study, we employed a physiologically-based pharmacokinetic (PBPK) modeling, as a tool for precision dosing, to predict the biological exposure of ceftriaxone in a virtually-constructed healthy and chronic kidney disease patient populations, with subsequent dosing optimizations. We started developing the model by integrating the physicochemical properties of the drug with biological system information in a PBPK software platform. A PBPK model in an adult healthy population was developed and evaluated visually and numerically with respect to experimental pharmacokinetic data. The model performance was evaluated based on the fold error criteria of the predicted and reported values for different pharmacokinetic parameters. Then, the model was applied to predict drug exposure in CKD patient populations with various degrees of severity. Results: The developed PBPK model was able to precisely describe the pharmacokinetic behavior of ceftriaxone in adult healthy population and in mild, moderate, and severe CKD patient populations. Decreasing the dose by approximately 25% in mild and 50% in moderate to severe renal disease provided a comparable exposure to the healthy population. Based on the simulation of multiple dosing regimens in severe CKD population, it has been found that accumulation of 2 g every 24 h is lower than the accumulation of 1 g every 12 h dosing regimen. Discussion: In this study, the observed concentration time profiles and pharmacokinetic parameters for ceftriaxone were successfully reproduced by the developed PBPK model and it has been shown that PBPK modeling can be used as a tool for precision dosing to suggest treatment regimens in population with renal impairment.

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

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