Current Drug Metabolism - Volume 21, Issue 10, 2020

Background: In vitro-in vivo extrapolation (IVIVE) of hepatic drug clearance (CL) involves the scaling of hepatic intrinsic clearance (CLint,uH) by functional liver size, which is approximated by total liver volume (LV) as per the convention. However, in most overweight and obese patients, LV includes abnormal liver fat, which is not thought to contribute to drug elimination, thus overestimating drug CL. Therefore, lean liver volume (LLV) might be a more appropriate scaler of CLint,uH. Objective: The objective of this work was to assess the application of LLV in CL extrapolation in overweight and obese patients (BMI >25 kg/m2) using a model drug antipyrine. Methods: Recently, a model to predict LLV from patient sex, weight, and height was developed and evaluated. In order to assess the LLV model’s use in IVIVE, a correlation-based analysis was conducted using antipyrine as an example drug. Results: In the overweight group (BMI >25 kg/m2), LLV could describe 36% of the variation in antipyrine CL (R² = 0.36), which was >2-fold higher than that was explained by LV (R² = 0.17). In the normal-weight group (BMI ≤25 kg/m2), the coefficients of determination were 58% (R² = 0.58) and 43% (R²= 0.43) for LLV and LV, respectively. ; Conclusion: The analysis indicates that LLV is potentially a more appropriate descriptor of functional liver size than LV, particularly in overweight individuals. Therefore, LLV has a potential application in IVIVE of CL in obesity.

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Background: As a metabolic and lifestyle disorder, diabetes mellitus poses a prodigious health risk. Out of the many key targets, DPP-IV is one of the very imperative therapeutic targets for the treatment of diabetic patients. Methods: In our current study, we have done the in silico simulations of ADME-T properties for naturally originated potent DPP-IV inhibitors like quinovic acid, stigmasterol, quinovic acid-3-beta-D-glycopyranoside, zygophyloside E, and lupeol. Structural topographies associated with different pharmacokinetic properties have been systematically assessed. Results: Glycosylation on quinovic acid is found to be noteworthy for the improvement of pharmacokinetic and toxicological properties, which leads to the prediction that zygophyloside E can be further tailored down to get the lead DPP-IV inhibitor. Conclusion: This assessment provides useful insight into the future development of novel drugs for the treatment of diabetes mellitus.

With the improvement of the global food safety regulatory system, there is an increasing importance for food safety risk assessment. Veterinary drugs are widely used in poultry and livestock products. The abuse of veterinary drugs seriously threatens human health. This article explains the necessity of risk assessment for veterinary drug residues in meat products, describes the principles and functions of risk assessment, then summarizes the risk assessment process of veterinary drug residues, and then outlines the qualitative and quantitative risk assessment methods used in this field. We propose the establishment of a new meat product safety supervision model with a view to improve the current meat product safety supervision system.

Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.

Antioxidants are molecules that can prevent damages to cells caused by free radicals. Recent studies also demonstrated that antioxidants play roles in preventing diseases. However, the number of known molecules with antioxidant activity is very small. Therefore, it is necessary to identify antioxidants from various resources. In the past several years, a series of computational methods have been proposed to identify antioxidants. In this review, we briefly summarized recent advances in computationally identifying antioxidants. The challenges and future perspectives for identifying antioxidants were also discussed. We hope this review will provide insights into researches on antioxidant identification.

Aims: Because of the high affinity of these animal neurotoxin proteins for some special target site, they were usually used as pharmacological tools and therapeutic agents in medicine to gain deep insights into the function of the nervous system. Background and Objective: The animal neurotoxin proteins are one of the most common functional groups among the animal toxin proteins. Thus, it was very important to characterize and predict the animal neurotoxin proteins. Methods: In this study, the differences between the animal neurotoxin proteins and non-toxin proteins were analyzed. Result: Significant differences were found between them. In addition, the support vector machine was proposed to predict the animal neurotoxin proteins. The predictive results of our classifier achieved the overall accuracy of 96.46%. Furthermore, the random forest and k-nearest neighbors were applied to predict the animal neurotoxin proteins. Conclusion: The compared results indicated that the predictive performances of our classifier were better than other two algorithms.

Background: Statins are effective for patients with decreased low-density lipoprotein therapy. Objective: The aim is to compare atorvastatin versus rosuvastatin on secondary percutaneous coronary intervention (PCI) rate and explore risk factors in coronary heart disease (CHD) patients. Methods: A cohort study with 283 CHD subjects was launched from 2011 to 2015. Cox proportional hazards regression model, Receiver Operating Characteristic (ROC) and nomogram were used to compare the effect of atorvastatin and rosuvastatin on secondary PCI rate and disease risk factors. Even why the two statins had different effects based on gene expression profile analysis has been explored. Results: Gene FFA (Freely fatty acid), AST (Aspartate Transaminase) and ALT (Alanine transaminase) showed the statistical difference between the four statin groups (P<0.05). In the AA group (Continuous Atorvastatin usage), albumin was a risk factor (Hazard Ratio (HR):1.076, 95%CI (1.001, 1.162), p<0.05). In the AR group (Start with Atorvastatin usage, then change to Rosuvastatin usage), ApoA was a protective factor (HR:0.004, 95%CI (0.001, 0.665), p<0.05). GLB (Galactosidase Beta) was a risk factor (HR:1.262, 95%CI (1.010, 1.576), p<0.05). In RR group (Continuous Rosuvastatin usage), ApoE was a protective factor (HR:0.943, 95%CI (0.890, 1.000), p<0.05). ALT was a risk factor (HR:1.030, 95%CI (1.000, 1.060), p<0.05). Conclusion: Patients in the RA group had the lowest secondary PCI rate. ALT was a risk factor in the RR group. Gene Gpt (Glutamic Pyruvic Transaminase) encoded for one subtype of ALT had a significantly different expression in different statin groups.