Validating ADME QSAR Models Using Marketed Drugs

2021 ◽  
pp. 247255522110175
Author(s):  
Vishal Siramshetty ◽  
Jordan Williams ◽  
Ðắc-Trung Nguyễn ◽  
Jorge Neyra ◽  
Noel Southall ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Chemical Structure ◽  
Scientific Literature ◽  
Aqueous Solubility ◽  
Quantitative Structure Activity Relationship ◽  
Artificial Membrane ◽  
Structure Activity ◽  
Qsar Models ◽  
Permeability Assay ◽  
Tier I

Problems with drug ADME are responsible for many clinical failures. By understanding the ADME properties of marketed drugs and modeling how chemical structure contributes to these inherent properties, we can help new projects reduce their risk profiles. Kinetic aqueous solubility, the parallel artificial membrane permeability assay (PAMPA), and rat liver microsomal stability constitute the Tier I ADME assays at the National Center for Advancing Translational Sciences (NCATS). Using recent data generated from in-house lead optimization Tier I studies, we update quantitative structure–activity relationship (QSAR) models for these three endpoints and validate in silico performance against a set of marketed drugs (balanced accuracies range between 71% and 85%). Improved models and experimental datasets are of direct relevance to drug discovery projects and, together with the prediction services that have been made publicly available at the ADME@NCATS web portal ( https://opendata.ncats.nih.gov/adme/ ), provide important tools for the drug discovery community. The results are discussed in light of our previously reported ADME models and state-of-the-art models from scientific literature. Graphical Abstract [Figure: see text]

scholarly journals Prediction of Terpenoids Toxicity Based on Quantitative Structure-Activity Relationships Model

2019 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Miguel X. Fernandes ◽  
José S. Câmara
Keyword(s):  
Chemical Structure ◽  
Vibrio Fischeri ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Geranic Acid ◽  
Structure Activity ◽  
Coefficient Corrélation ◽  
Qsar Models ◽  
Toxicity Level

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13) and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites which chemical structure is highly diverse. A quantitative structure-activity relationship (QSAR) model to predict the terpenoids toxicity and to evaluate the influences of their chemical structure, was developed in this study, by assessing the toxicity of 27 terpenoid standards using Gram-negative bioluminescent Vibrio fischeri, in real time. Under the test conditions, at concentration of 1 µM, the terpenoids showed a toxicity level lower than five %, with exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30 % at concentration of 50 to 100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene and linalool oxide. Regarding the functional group, the terpenoids toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. CODESSA software was employed to develop the QSAR models based on the correlation of terpenoids toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoids toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., max partial charge for a C atom [Zefirov's PC]) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with training and test coefficient correlation higher than 0.810 and 0.535, respectively, and square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are a suitable and a rapid tool to predict the terpenoids toxicity in a diversity of food products.

scholarly journals Quantitative Structure Activity Relationship for Drug Discovery

2019 ◽  
Vol 8 (9S) ◽  
pp. 607-610
Keyword(s):  
Drug Discovery ◽  
Biological Activities ◽  
Principal Component ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Qsar Models ◽  
Pharmaceutical Industries

Quantitative structure-activity relationship (QSAR), gives useful information for drug design and medicinal chemistry. QSAR is a method used to anticipate the organic reaction of a molecule by developing equations which use descriptors calculated from its compounds. The molecular descriptors vary in complexity. A time consuming and expensive process for pharmaceutical industries is drug discovery. An inspiration driving these QSAR models is to help revive the revelation of molecular drug candidates through minimized test work and to bring a drug to market faster. To obtain sorted features principal component analysis is used. The biological activities of the test set are determined by training the neural network using training set. By predicting the activities it can be known whether the drug is close to the target or not.

CLINICAL AND TOXICOLOGICAL RELEVANCE OF CYP2C9: Drug-Drug Interactions and Pharmacogenetics

2005 ◽  
Vol 45 (1) ◽  
pp. 477-494 ◽  
Author(s):  
Allan E. Rettie ◽  
Jeffrey P. Jones
Keyword(s):  
Oral Anticoagulants ◽  
Quantitative Structure Activity Relationship ◽  
Structural Basis ◽  
Cytochrome P450 Enzyme ◽  
Metabolic Clearance ◽  
Structure Activity ◽  
Oral Hypoglycemics ◽  
P450 Enzyme ◽  
Qsar Models ◽  
Inhibitor Selectivity

CYP2C9 is a major cytochrome P450 enzyme that is involved in the metabolic clearance of a wide variety of therapeutic agents, including nonsteroidal antiinflammatories, oral anticoagulants, and oral hypoglycemics. Disruption of CYP2C9 activity by metabolic inhibition or pharmacogenetic variability underlies many of the adverse drug reactions that are associated with the enzyme. CYP2C9 is also the first human P450 to be crystallized, and the structural basis for its substrate and inhibitor selectivity is becoming increasingly clear. New, ultrapotent inhibitors of CYP2C9 have been synthesised that aid in the development of quantitative structure-activity relationship (QSAR) models to facilitate drug redesign, and extensive resequencing of the gene and studies of its regulation will undoubtedly help us understand interindividual variability in drug response and toxicity controlled by this enzyme.

scholarly journals Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model

Foods ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 628 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Miguel X. Fernandes ◽  
José S. Câmara
Keyword(s):  
Vibrio Fischeri ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Partial Charge ◽  
Quantitative Structure ◽  
Chemical Structures ◽  
Structure Activity ◽  
Qsar Models ◽  
Toxicity Level

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13), and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50–100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov’s partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.

QSAR modeling for reaction rate constants of eaq− with diverse organic compounds in water

2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei
Keyword(s):  
Organic Compounds ◽  
Reaction Rate ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Reaction Rate Constants ◽  
Structure Activity ◽  
Qsar Models ◽  
The Impact

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.

Understanding the quantitative structure–activity relationship of acetylcholinesterase inhibitors for the treatment of Alzheimer's disease

2015 ◽  
Vol 14 (06) ◽  
pp. 1550040 ◽  
Author(s):  
Anuradha Sharma ◽  
Poonam Piplani
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Structural Basis ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Qsar Models ◽  
Relationship Of

Alzheimer's disease (AD) is the most common cause of dementia in old aged people and clinically used drugs for treatment are associated with side effects. Thus, there is a current demand for the discovery and development of new potential molecules. However, the recent advances in drug therapy have challenged the predominance of the disease. In this manuscript, an attempt has been made to develop the 2D and 3D quantitative structure–activity relationship (QSAR) models for a series of rutaecarpine, quinazolines and 7,8-dehydrorutaecarpine derivatives to obtain insights to Acetylcholinesterase (AChE) inhibition. Five different QSAR models have been generated and validated using a set of 52 compounds comprising of varying scaffolds with IC50 values ranging from 11,000 nM to 0.6 nM. These AChE-specific prediction models (M1–M5) adequately reflect the structure–activity relationship of the existing AChE inhibitors. Out of all developed models, QSAR model generated using ADME properties has been found to be the best with satisfactory statistical significance (regression (r2) of 0.9309 and regression adjusted coefficient of variation [Formula: see text] of 0.9194). The QSAR models highlight the importance of aromatic moiety as their presence in the structure influence the biological activity. Additional insights on the compounds show that acyclic amines attached to side chain have lower activity than cyclic amines. The QSAR models pinpointing structural basis for the AChEIs suggest new guidelines for the design of novel molecules.

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