Pathways of Reactive Metabolite Formation with Toxicophores/-Structural Alerts. Reactive Drug Metabolites. Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA; 2012. pp. 93-129. [DOI: 10.1002/9783527655748.ch5]

Artificial Intelligence in Drug Safety and Metabolism

The use of artificial intelligence methods in drug safety began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been endlessly expanding ever since and the models have become more complex. These approaches are now integrated into molecule risk assessment processes along with in vitro and in vivo methods. Today, artificial intelligence can be used in every phase of drug discovery and development, from profiling chemical libraries in early discovery, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life cycle management. This chapter provides an overview of artificial intelligence in drug safety and describes its application throughout the entire discovery and development process.

2-aminothiazoles in drug discovery: Privileged structures or toxicophores?

The 2-aminothiazole functionality has long been established as a privileged structural feature and therefore frequently exploited in the process of drug discovery and development. It has been introduced into numerous compounds due to its capacity for targeting a wide range of therapeutic target proteins. On the other hand, the aminothiazole group has also been classified as a toxicophore susceptible to metabolic activation and the ensuing reactive metabolite formation, hence caution is warranted when used in drug design. This review is divided into three parts entailing: (i) the general characteristics of the aminothiazole group, (ii) the advantages of the aminothiazole group in medicinal chemistry, and (iii) the impact of the integrated aminothiazole group on compound safety profile.

Designing around Structural Alerts in Drug Discovery

Cumulative research over several decades has implicated the involvement of reactive metabolites in many idiosyncratic adverse drug reactions (IADRs). Consequently, "avoidance" strategies have been inserted into drug discovery paradigms, which include the exclusion of structural alerts and possible termination of reactive metabolite-positive compounds. Several noteworthy examples where reactive metabolite-related liabilities have been resolved through structure-metabolism studies are presented herein. Considerable progress has also been made in addressing the limitations of the avoidance strategy and further refining the process of managing reactive metabolite issues in drug development. These efforts primarily stemmed from the observation that numerous drugs, which contain structural alerts and/or form reactive metabolites, are devoid of ADRs. The Perspective also dwells into an analysis of the structural alert/reactive metabolite concept with a discussion of risk mitigation tactics to support the progression of reactive metabolite-positive drug candidates.

Computational Toxicology and Drug Discovery

The use of computational toxicology methods within drug discovery began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been continuously expanding ever since and the tasks at hand have become more complex. These approaches are now strategically integrated into the risk assessment process, as a complement to in vitro and in vivo methods. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life-cycle management. This chapter provides an overview of the field and describes the application of computational toxicology throughout the entire discovery and development process.

Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations

Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies implemented to determine toxicophores may be broadly classified into biological, bioanalytical and computational approaches. The biological approach involves analysis of bioactivated metabolites, whereas the computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique and a few toxicophore identification/estimation tools. Being one of the major steps in drug discovery process, toxicophore identification has proven to be an essential screening step in drug design and development. The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting and determining toxicophores with an emphasis on their scope and limitations. Such information may prove vital in the appropriate selection of methodology and can be used as screening technology by researchers to discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be eliminated or removed.