Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications.

Eravacycline is a broad-spectrum fluorocycline currently approved for complicated intra-abdominal infections (cIAIs). In lung-infection models, it is effective against methicillin-resistant Staphylococcus aureus (MRSA) and tetracycline-resistant MRSA. As such, we aimed to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model to evaluate eravacycline’s pulmonary distribution and kinetics. Data were extracted from a Phase I study ( NCT01989949 ) which assessed the bronchopulmonary disposition of intravenous eravacycline to construct the population PK model that could adequately describe the drug’s pulmonary kinetics. Eravacycline lung PK was best described by a three-compartment model with allometric scaling, with the epithelial lining fluid (ELF) component parameterized as the ELF distribution ratio ( R a t i o = C f r e e , E L F C f r e e , c e n t r a l , unbound concentration in ELF over central compartment). The estimated ELF distribution ratio was 8.26 (95% confidence interval = 6.8–9.8). Besides allometrically scaled weight, no other significant covariate was found. MIC ₉₀ was 0.5 mg/L ( Escherichia coli ), 2 mg/L ( Klebsiella pneumoniae ), 0.5 mg/L ( Acinetobacter baumannii ), and 0.12 mg/L ( S. aureus ). At the approved cIAI dosage or higher (1 mg/kg or 1.5 mg/kg q12h), a PK/PD cutoff value of 2 mg/L was appropriate for E. coli , while a lower value of 1 mg/L was selected for K. pneumoniae , A. baumannii , and S. aureus . For lower doses, the cutoff value was reduced to 0.5 mg/L for K. pneumoniae , A. baumannii , and S. aureus . The study showed eravacycline was widely distributed into the lungs with promising antibacterial efficacy, thus justifying further investigations into its uses for pulmonary infections.

Schizandrol A (SZA) and schizandrol B (SZB) are two active ingredients of Wuzhi capsule (WZC), a Chinese proprietary medicine commonly prescribed to alleviate tacrolimus (FK-506)-induced hepatoxicity in China. Due to their inhibitory effects on cytochrome P450 (CYP) 3A enzymes, SZA/SZB may display drug–drug interaction (DDI) with tacrolimus. To identify the extent of this DDI, the enzymes’ inhibitory profiles, including a 50% inhibitory concentration (IC₅₀) shift, reversible inhibition (RI) and time-dependent inhibition (TDI) were examined with pooled human-liver microsomes (HLMs) and CYP3A5-genotyped HLMs. Subsequently, the acquired parameters were integrated into a physiologically based pharmacokinetic (PBPK) model to quantify the interactions between the SZA/SZB and the tacrolimus. The metabolic studies indicated that the SZB displayed both RI and TDI on CYP3A4 and CYP3A5, while the SZA only exhibited TDI on CYP3A4 to a limited extent. Moreover, our PBPK model predicted that multiple doses of SZB would increase tacrolimus exposure by 26% and 57% in CYP3A5 expressers and non-expressers, respectively. Clearly, PBPK modeling has emerged as a powerful approach to examine herb-involved DDI, and special attention should be paid to the combined use of WZC and tacrolimus in clinical practice.

Background: Ganciclovir and valganciclovir are used for prophylaxis and treatment of cytomegalovirus infection. However, there is great interindividual variability in ganciclovir’s pharmacokinetics (PK), highlighting the importance of individualized dosing. To facilitate model-informed precision dosing (MIPD), this study aimed to establish a parametric model repository of ganciclovir and valganciclovir by summarizing existing population pharmacokinetic information and analyzing the sources of variability. (2) Methods: A total of four databases were searched for published population PK models. We replicated these models, evaluated the impact of covariates on clearance, calculated the probability of target attainment for each model based on a predetermined dosing regimen, and developed an area under the concentration–time curve (AUC) calculator using maximum a posteriori Bayesian estimation. (3) Results: A total of 16 models, one- or two-compartment models, were included. The most significant covariates were body size (weight and body surface area) and renal function. The results show that 5 mg/kg/12 h of ganciclovir could make the AUC_0–24h within 40–80 mg·h/L for 50.03% pediatrics but cause AUC_0–24h exceeding the exposure thresholds for toxicity (120 mg·h/L) in 51.24% adults. (4) Conclusions: Dosing regimens of ganciclovir and valganciclovir should be adjusted according to body size and renal function. This model repository has a broad range of potential applications in MIPD.