Observation of Clinically Relevant Drug Interaction in Chimeric Mice with Humanized Livers: The Case of Valproic Acid and Carbapenem Antibiotics

A Clinical Nomogram for Predicting Substandard Serum Valproic Acid Concentrations in Chinese Patients With Epilepsy

Background

It is well-known that substandard serum valproic acid (VPA) concentrations may lead to treatment failure of epilepsy. However, there is still a lack of a quick method to predict whether a patient's serum VPA concentration will reach the standard.

Objective

The aims of this study were to investigate the factors leading to substandard serum VPA concentrations in Chinese patients with epilepsy and develop a related nomogram for risk prediction.

Methods

From January 2019 to March 2022, a total of 1143 serum VPA concentrations were collected from 630 hospitalized Chinese patients with epilepsy who were monitored by the Department of Pharmacy of Lu'an People's Hospital, and complete clinical data were collected from the corresponding patients for retrospective analysis. All monitored serum VPA concentrations were further divided into a training cohort and a validation cohort. For the training cohort, serum VPA concentrations below 50 µg/mL and between 50 and 100 µg/mL were classified into the subtherapeutic group and therapeutic group, respectively. The variables were selected from the clinical data, and differences between the variables of the subtherapeutic and therapeutic groups were analyzed. The influencing factors leading to substandard serum VPA concentrations were screened via logistic regression analysis, and the screened influencing factors were used to establish the nomogram prediction model.

Results

Multivariate logistic regression analysis revealed that the daily dose per unit of body weight (mg/kg/d), route of administration, presence of hepatic lesions, hypoalbuminemia, and combination with carbapenems or barbiturates were independent factors influencing the occurrence of substandard serum VPA concentrations. On the basis of the results of the multivariate logistic regression analysis, a nomogram risk prediction model for substandard serum VPA concentration was established. The values of the C-index and internal verification results indicated that the nomogram model had good accuracy and discrimination. The decision curve revealed that the nomogram that predicted the risk of substandard serum VPA concentration had a greater net benefit value (ranging from 12% to 94%), indicating that the model had a wide prediction interval.

Conclusions

Our study established a nomogram risk prediction model for substandard serum VPA concentrations in Chinese patients with epilepsy, which can help doctors or patients control the serum VPA concentration within the target concentration range as soon as possible.

Effect of drug interactions between carbapenems and valproate on serum valproate concentration: a systematic review and meta-analysis

Background: Concurrent use of valproate and carbapenem antibiotics may decrease serum valproate concentration (SVC). This study evaluated the effects of carbapenem-valproate drug interactions. Research design and methods: We screened PubMed, EMBASE, and Cochrane databases for eligible prospective or retrospective studies that evaluated the effect of concurrent use of carbapenem and valproate compared with valproate alone on SVC. Primary outcomes were the change in SVC from before the addition of the carbapenem to the SVC during the use of carbapenems and after carbapenem discontinuation, and seizure-related outcomes. Secondary outcomes were the influence of valproate or carbapenem dose on SVC and Drug Interaction Probability Scale scores. Results: Twelve studies (633 patients) were included. Compared with valproate alone, combination treatment with carbapenem substantially decreased mean SVC (mean difference, -43.98 mg/L; 95% confidence interval, -48.18 to -39.78). The onset of SVC decreases was within 1-3 days following carbapenem initiation. Seizure frequency increased by 26.3% during combination treatment. No difference was found in mean SVC between the different doses of valproate or carbapenem during combination treatment. Mean SVC increased to similar pre-carbapenem level within 1 to 2 weeks after carbapenem discontinuation. Conclusions: The drug interaction between valproate and carbapenem causes substantial SVC decreases, even to subtherapeutic levels, which may increase the risk of seizures.

Attenuated P. falciparum Parasite Shows Cytokine Variations in Humanized Mice

A recently developed humanized mouse has been used to assess the immune response evoked against the isolated attenuated C9 parasite clone (C9-M; carrying a single insertion disrupting the open reading frame (ORF) of PF3D7_1305500) of Plasmodium falciparum. Significant human RBC engraftment was achieved by ameliorating the residual non-adaptive immune response using clodronate-loaded liposome treatment. Controlled reactive professional phagocytic leukocytes in immunodeficient mice allowed for sizeable human blood chimerism and injected huRBCs acted as bona fide host cells for P. falciparum. huRBC-reconstituted immunodeficient mice received infectious challenge with attenuated P. falciparum C9 parasite mutants (C9-M), complemented (C9-C), and wild type (NF54) progenitors to study the role of immune effectors in the clearance of the parasite from mouse circulation. C9-M and NF54 parasites grew and developed in the huRBC-reconstituted humanized NSG mice. Further, the presence of mutant parasites in deep-seated tissues suggests the escape of parasites from the host's immune responses and thus extended the survival of the parasite. Our results suggest an evasion mechanism that may have been employed by the parasite to survive the mouse's residual non-adaptive immune responses. Collectively, our data suggest that huRBCs reconstituted NSG mice infected with attenuated P. falciparum is a valuable tool to explore the role of C9 mutation in the growth and survival of parasite mutants and their response to the host's immune responses. This mouse might help in identifying novel chemotherapeutic targets to develop new anti-malarial drugs.

Characterization of plasma protein binding in two mouse models of humanized liver, PXB mouse and humanized TK-NOG mouse

The unbound fractions in plasma (f _up) in two mouse models of humanized liver mice, PXB and humanized TK-NOG mice, were compared with human f _up values using equilibrium dialysis method. A good relationship between f _up values obtained from PXB mice and humans was observed; the f _up of 34/39 compounds (87.2%) in PXB mice were within 3-fold of human f _up. In contrast, a weak correlation was observed between human and humanized TK-NOG mouse f _up values; the f _up of 15/24 compounds (62.5%) in humanized TK-NOG mice were within 3-fold of human f _up. As different profiles of plasma protein binding (PPB) profiles were observed between PXB and humanized TK-NOG mice, f _up evaluation is necessary in each mouse model to utilize these humanized liver mice for pharmacological, drug-drug interaction (DDI), and toxicity studies. The unbound fraction in the mixed plasma of human and SCID mouse plasma (85:15) was well correlated with f _up in PXB mice (38/39 compounds within a 3-fold). Thus, this artificial PXB mouse plasma could be used to evaluate PPB.

P450-Humanized and Human Liver Chimeric Mouse Models for Studying Xenobiotic Metabolism and Toxicity

Preclinical evaluation of drug candidates in experimental animal models is an essential step in drug development. Humanized mouse models have emerged as a promising alternative to traditional animal models. The purpose of this mini-review is to provide a brief survey of currently available mouse models for studying human xenobiotic metabolism. Here, we describe both genetic humanization and human liver chimeric mouse models, focusing on the advantages and limitations while outlining their key features and applications. Although this field of biomedical science is relatively young, these humanized mouse models have the potential to transform preclinical drug testing and eventually lead to a more cost-effective and rapid development of new therapies.

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.