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Heterocyclic Drugs from Plants
Published in Rohit Dutt, Anil K. Sharma, Raj K. Keservani, Vandana Garg, Promising Drug Molecules of Natural Origin, 2020
Debasish Bandyopadhyay, Valeria Garcia, Felipe Gonzalez
While studying pyrrole derivatives, the structure-activity relationship abbreviated as SAR should be emphasized. SAR correlates the structure and medicinal activity of a molecule, which is a mandatory requisite in the pharmaceutical industry. SAR can easily identify the important functional groups and sub-units that play a major role to determine the medicinal effect of a drug. Efforts were taken to develop pyrrole-derived drugs to remove/reduce the harmful secondary responses of many current anti-diabetic drugs. Some anti-diabetic drugs work as dipeptidyl peptidase IV (DPP4) inhibitors. Attempts were taken to reduce the aftereffects through incorporation of pyrrole group to the inhibitor molecule (Mohamed et al., 2014). Replacement of a thienopyrimidine with a pyrrolopyrimidine in the dipeptidyl peptidase IV inhibitor increases activity as well as stability. The activity of these pyrrole derivatives was compared to a widely used medication for diabetes, glimepiride. The pyrrole derivatives showed similar therapeutic activity.
Quick Methods: Structure-Activity Relationships and Short-Term Bioassay
Published in Samuel C. Morris, Cancer Risk Assessment, 2020
Early groundwork for subsequent work on SAR was laid in the 1870s (Kland, 1977). Since the 1930s, many useful drugs have been developed by applying knowledge of SAR (Craig and Enslein, 1981). The field has become very sophisticated and is an important method of developing new and more effective drugs today. Use of SAR in drug development, however, involves exploring modifications to chemicals for which much information is available concerning biological effect and the relationship between molecular structure and that effect.
Hazard Characterization and Dose–Response Assessment
Published in Ted W. Simon, Environmental Risk Assessment, 2019
The five (Q)SAR validation principles are as follows: a defined endpoint;an unambiguous algorithm;a defined domain of applicability;appropriate measures of goodness of fit, robustness, and predictivity;a mechanistic interpretation, if possible. (Q)SAR provides a means to understand the link between chemical structure and biological activity as a means for preliminary screening of chemicals.210,211 The field has grown considerably, with increasing reliance on data mining, statistics, and artificial intelligence. As part of an overall strategy to address chemical hazards, prediction models need to be validated whether these models are based on chemical properties or in vitro testing results.212
Enhancing global and local decision making for chemical safety assessments through increasing the availability of data
Published in Toxicology Mechanisms and Methods, 2023
Adrian Fowkes, Robert Foster, Steven Kane, Andrew Thresher, Anne-Laure Werner, Antonio Anax F. de Oliveira
The value of toxicity data does not just lie on the data per se but also the knowledge that it helps create. In addition to the knowledge gained by toxicologists, the data can also train computational models or be used for read-across. (Q)SAR models are an effective way to express knowledge from historical data and robustly apply it to predict the properties of new chemical entities. (Q)SAR models fall into two main camps (Barber et al. 2017): expert-based, which include rules defined by experts based on an understanding of the supporting data to generate predictions analogous to the decision-making process of a human expert, and statistical-based, which identify relationships by running algorithms over training data (Ponting et al. 2022). In both cases, the quality of the original data is paramount for each system to enable accurate predictions. Furthermore, once each system has reached a level of maturity, improvements to the chemical space coverage and predictivity are primarily driven by the quality and breadth of the training data. The acquisition of new data to improve (Q)SAR models reduces instances where model outputs will require the assessor to generate additional data or continue a safety assessment with an incorrect prediction.
Dietary Phytochemicals as a Potential Source for Targeting Cancer Stem Cells
Published in Cancer Investigation, 2021
Prasath Manogaran, Devan Umapathy, Manochitra Karthikeyan, Karthikkumar Venkatachalam, Anbu Singaravelu
Phytochemicals and enriched natural extracts able to interfere with self-renewal and drug resistance pathways in CSCs have been identified based on structural activity relationship (SAR) Table 1. SAR plays a vital role in the development of drugs in the initial stage and to find active molecules structure have based on functional moiety (Hydroxyl/Methoxy/Hydroxymethyl/Allyl) present in the molecular structure. Further, selected phytochemicals in the present review modulate the transcription factors which regulate the self-renewal and survival of CSCs. This is a signpost of improvement of cancer treatment because the synthetic anticancer drugs that are currently used are often highly toxic for normal cells and induced adverse effects. These phytochemicals in combination with other chemotherapeutic drugs lead to a synergistic anticancer effect and reduce side effects. Besides, natural compounds are much better than the synthetic drugs which are available in the market. Finally, it is an effective data to understand the molecular mechanism of phytochemicals induced toxicity against self-renewal and survival of human cancer stem cells.
Using artificial intelligence methods to speed up drug discovery
Published in Expert Opinion on Drug Discovery, 2019
Óscar Álvarez-Machancoses, Juan Luis Fernández-Martínez
The optimally connection of in vitro pharmacology profiling to the later development process of a drug is one of the major challenges within the pharmaceutical industry, costing billions of dollars. In the initial stages of the drug discovery process, the main objective is to identify potential drugs, their hazards and their affinity to a given target or targets. This data provided by this first initial screen can be utilized in later phases for early decision-making. This initial hazard identification, known as profiling, can be used in the lead optimization phase to eliminate the liability by constructing structure–activity relationship (SAR) models. SAR models are of superior importance in preclinical trials as they are used to select key candidates to progress into development and design in vivo studies. Importantly, it is desirable to obtain a comprehensive understanding of the broad pharmacological profile of the drug candidate before carrying out first-in-human trials [61–63]. In this sense, the design and development of AI algorithms based on drug profiling and understanding the MOA of drugs is a great importance in the prediction of side effects. Consequently, by connecting the results of preclinical models with the observations made in clinical trials wil not only aid reducing the associated costs of drug development, but also reduce safety-related drug attrition rates [42].