Testing computational toxicology models with phytochemicals

Predicting phase-I metabolism of piceatannol: an in silico study

Piceatannol is a natural compound found in plants and can be derived from resveratrol. While resveratrol has been extensively researched for its effects and how the body processes it, there are concerns about its use. These concerns include its limited absorption in the body, the need for specific dosages, potential interactions with other drugs, lack of standardization, and limited clinical evidence to support its benefits. Interestingly, Piceatannol, another compound derived from resveratrol, has received less attention from researchers but appears to offer advantages. It has better bioavailability and seems to have a more favorable therapeutic profile compared to resveratrol. Surprisingly, no previous attempts have been made to explore or predict the metabolites of piceatannol when it interacts with the enzyme cytochrome P450. This study aims to fill that gap by predicting how piceatannol is metabolized by cytochrome P450 and assessing any potential toxicity associated with its metabolites. This research is interesting because it's the first of its kind to investigate the metabolic fate of piceatannol, especially in the context of cytochrome P450. The findings have the potential to significantly contribute to the field of piceatannol research, particularly in the food industry where this compound has applications and implications.

Graphical abstract

Pharmacoinformatics approaches in the discovery of drug-like antimicrobials of plant origin

Medicinal plants have served as an important source for addressing the ailments of humans and animals alike. The emergence of advanced technologies in the field of drug discovery and development has helped in isolating various bioactive phytochemicals and developing them as drugs. Owing to their significant pharmacological benefits and minimum adverse effects, they not only serve as good candidates for therapeutics themselves but also help in the identification and development of related drug like molecules against various metabolic and infectious diseases. The ever-increasing diversity, severity and incidence of infectious diseases has resulted in an exaggerated mortality and morbidity levels. Geno-proteomic mutations in microbes, irrational prescribing of antibiotics, antimicrobial resistance and human population explosion, all call for continuous efforts to discover and develop alternated therapeutic options against the microbes. This review article describes the pharmacoinformatics tools and methods which are currently used in the discovery of bioactive phytochemicals, thus making the process more efficient and effective. The pharmacological aspects of the drug discovery and development process have also been reviewed with reference to the in silico activities. Communicated by Ramaswamy H. Sarma.

In silico genotoxicity and carcinogenicity prediction for food-relevant secondary plant metabolites

Humans are exposed to thousands of different secondary plant metabolites which may have beneficial health effects, but numerous compounds may also have toxic potential. In the present study we have examined the genotoxic and carcinogenic potential of 609 food-relevant phytochemicals by using computer models for toxicity prediction. We developed a scoring method and combined the results of different models to increase the predictive power. A combination of the VEGA models SARpy, KNN, ISS, and CAESAR, and of the LAZAR model "Salmonella typhimurium" for genotoxicity prediction performed better than the single models regarding specificity and accuracy. Statistical evaluation of the combined model for carcinogenicity prediction was not possible due to the low number of substances suitable for model validation. The in silico results of the present exercise will be useful for priority setting purposes regarding future risk assessment of secondary plant metabolites. Based on our analysis, (-)-asimilobine, aloin, annoretine, chrysothrone, coptisine, elymoclavine, and thalicminine were predicted to be genotoxic with high probability and may therefore be selected for subsequent experimental genotoxicity testing. Moreover, the class of pyrrolizidine alkaloids is suggested to be a high priority subject for further studies as these substances have been predicted to be carcinogenic with high probability.

Phytomedicine polypharmacology: Cancer therapy through modulating the tumor microenvironment and oxylipin dynamics

Integrative approaches in cancer therapy have recently been extended beyond the induction of cytotoxicity to controlling the tumor microenvironment and modulating inflammatory cascades and pathways such as lipid mediator biosynthesis and their dynamics. Profiling of important lipid messengers, such as oxylipins, produced as part of the physiological response to pharmacological stimuli, provides a unique opportunity to explore drug pharmacology and the possibilities for molecular management of cancer physiopathology. Whereas single targeted chemotherapeutic drugs commonly lack efficacy and invoke drug resistance and/or adverse effects in cancer patients, traditional herbal medicines are seen as bright prospects for treating complex diseases, such as cancers, in a systematic and holistic manner. Understanding the molecular mechanisms of traditional medicine and its bioactive chemical constituents may aid the modernization of herbal remedies and the discovery of novel phytoagents for cancer management. In this review, systems-based polypharmacology and studies to develop multi-target drugs or leads from phytomedicines and their derived natural products that may overcome the problems of current anti-cancer drugs, are proposed and summarized.

Toxicological evaluations of methanolic extract of Moringa oleifera leaves in liver and kidney of male Wistar rats

BACKGROUND

This study was conducted to investigate toxicological effects associated with prolonged consumption of Moringa oleifera leaves as a beverage.

METHODS

Thirty rats were used in this study. They were grouped into five groups of six rats. Rats in group I received 2 mL/kg body weight (b.w.) of corn oil (vehicle). Animals in groups II, III, IV and V received 50, 100, 200 and 400 mg/kg b.w. of methanolic extract of M. oleifera (MEMO) for 8 weeks. Serum collected was analyzed for alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein, albumin, globulin, blood urea nitrogen (BUN) and creatinine.

RESULTS

There was a significant (p<0.05) increase in serum total protein and globulin in a dose-dependent manner. Rats that received MEMO at 200 and 400 mg/kg b.w. showed a significant (p<0.05) increase in serum ALT, AST, BUN and creatinine which pointed to hepatic and kidney damage. All experimental animals that received MEMO had a significant (p<0.05) increase in body weight in a dose-dependent manner.

CONCLUSIONS

This study therefore confirms for the first time that chronic administration of M. oleifera leaves might predispose to hepatic and kidney damage.

Bioinformatics opportunities for identification and study of medicinal plants

Plants have been used as a source of medicine since historic times and several commercially important drugs are of plant-based origin. The traditional approach towards discovery of plant-based drugs often times involves significant amount of time and expenditure. These labor-intensive approaches have struggled to keep pace with the rapid development of high-throughput technologies. In the era of high volume, high-throughput data generation across the biosciences, bioinformatics plays a crucial role. This has generally been the case in the context of drug designing and discovery. However, there has been limited attention to date to the potential application of bioinformatics approaches that can leverage plant-based knowledge. Here, we review bioinformatics studies that have contributed to medicinal plants research. In particular, we highlight areas in medicinal plant research where the application of bioinformatics methodologies may result in quicker and potentially cost-effective leads toward finding plant-based remedies.

In silico toxicology models and databases as FDA Critical Path Initiative toolkits

In silico toxicology methods are practical, evidence-based and high throughput, with varying accuracy. In silico approaches are of keen interest, not only to scientists in the private sector and to academic researchers worldwide, but also to the public. They are being increasingly evaluated and applied by regulators. Although there are foreseeable beneficial aspects--including maximising use of prior test data and the potential for minimising animal use for future toxicity testing--the primary use of in silico toxicology methods in the pharmaceutical sciences are as decision support information. It is possible for in silico toxicology methods to complement and strengthen the evidence for certain regulatory review processes, and to enhance risk management by supporting a more informed decision regarding priority setting for additional toxicological testing in research and product development. There are also several challenges with these continually evolving methods which clearly must be considered. This mini-review describes in silico methods that have been researched as Critical Path Initiative toolkits for predicting toxicities early in drug development based on prior knowledge derived from preclinical and clinical data at the US Food and Drug Administration, Center for Drug Evaluation and Research.