A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets

2003 ◽  
Vol 31 (2) ◽  
pp. 437-443 ◽  
Author(s):  
E.E. Schadt ◽  
S.A. Monks ◽  
S.H. Friend
Keyword(s):  
Drug Discovery ◽  
Complex Traits ◽  
Drug Targets ◽  
Drug Discovery Process ◽  
New Paradigm ◽  
Molecular Phenotype ◽  
Clinical Genetic ◽  
Clinical Trait ◽  
Phenotype Data ◽  
The Many

Application of statistical genetics approaches to variations in mRNA transcript abundances in segregating populations can be used to identify genes and pathways associated with common human diseases. The combination of this genetic information with gene expression and clinical trait data can also be used to identify subtypes of a disease and the genetic loci specific to each subtype. Here we highlight results from some of our recent work in this area and further explore the many possibilities that exist in employing a more comprehensive genetics and functional genomics approach to the functional annotation of genomes, and in applying such methods to the validation of targets for complex traits in the drug discovery process.

scholarly journals Drug Discovery Inspired by Mother Nature: Seeking Natural Biochemotypes and the Natural Assembly Rules of the Biochemome

2013 ◽  
Vol 16 (2) ◽  
pp. 331 ◽  
Author(s):  
Qiong Gu ◽  
Xin Yan ◽  
Jun Xu
Keyword(s):  
Drug Discovery ◽  
Periodic Table ◽  
Drug Targets ◽  
Biological Activities ◽  
Building Blocks ◽  
Genome Project ◽  
New Paradigm ◽  
Chemical Library ◽  
Chemical Structures ◽  
Natural Building

Purpose. The Human Genome Project is producing a new biological ‘periodic table’, which defines all genes for making macromolecules (proteins, DNA, RNA, etc) and the relations between genes and their biological functions. We now need to consider whether to initiate a biochemome project aimed at discovering biochemistry’s ‘periodic table’, which would define all molecular parts for making small molecules (natural products) and the relations between the parts and their functions to regulate genes. By understanding the Biochemome, we might be able to design biofunctional molecules based upon a set of molecular parts for drug innovation. Methods. A number of algorithms for processing chemical structures are used to systematically derive chemoyls (natural building blocks) from a database of compounds identified in Traditional Chinese Medicine (TCM). The rules to combine chemoyls for biological activities are then deduced by mining an annotated TCM structure-activity database (ATCMD). Based upon the rules and the basic chemoyls, a chemical library can be biochemically profiled, virtual synthetic routes can be planned, and lead compounds can be identified for a specific drug target. Conclusions. The Biochemome is the complete set of molecular components (chemoyls) in an organism and Biochemomics studies the rules governing their assembly and their evolution, together with the relations between the Biochemome and drug targets. This approach provides a new paradigm for drug discovery that is based on a comprehensive knowledge of the synthetic origins of biochemical diversity, and helps to direct biomimetic syntheses aimed at assembling quasi-natural product libraries for drug screening.   This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

scholarly journals Hunting for cures: Genomics and new diseasecontrol strategies

2003 ◽  
Vol 25 (6) ◽  
pp. 19-21
Author(s):  
Michael Ginger
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
New Drugs ◽  
Potential Drug ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Genomic Technologies ◽  
Potential Drug Targets

New drugs are needed urgently to win the war against parasites that cause many serious diseases that are endemic or resurgent in some of the World's poorest countries. Post-genomic technologies provide a powerful resource that can be exploited during the drug-discovery process. With genome sequencers able to uncover secrets from even the most experimentally intractable of pathogens, the complete and annotated genomes from a number of the most medically important parasites are now, or will soon be, published. Already, the information that has been released from these projects has been put to good use in identifying new potential drug targets.

Screening of Natural Lead Molecules Against Putative Molecular Targets of Drug-resistant Cryptococcus spp: An Insight from Computer-aided Molecular Design

2019 ◽  
Vol 18 (31) ◽  
pp. 2681-2701
Author(s):  
Meghna Manjunath ◽  
Sinosh Skariyachan
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Targets ◽  
Medicinal Chemistry ◽  
Fungal Infections ◽  
Limited Information ◽  
New Paradigm ◽  
Effective Prevention ◽  
Computer Aided

Cryptococcosis is one of the major invasive fungal infections distributed worldwide with high mortality rate. C. neoformans and C. gattii are the major organisms that cause various types of infections. Anti-fungal resistances exhibited by the mentioned species of Cryptococcus threaten their effective prevention and treatment. There is limited information available on human to human transmission of the pathogen and virulent factors that are responsible for Cryptococcus mediated infections. Hence, there is high scope for understanding the mechanism, probable drug targets and scope of developing natural therapeutic agents that possess high relevance to pharmaceutical biotechnology and medicinal chemistry. The proposed review illustrates the role of computer-aided virtual screening for the screening of probable drug targets and identification of natural lead candidates as therapeutic remedies. The review initially focuses on the current perspectives on cryptococcosis, major metabolic pathways responsible for the pathogenesis, conventional therapies and associated drug resistance, challenges and scope of structure-based drug discovery. The review further illustrates various approaches for the prediction of unknown drug targets, molecular modeling works, screening of natural compounds by computational virtual screening with ideal drug likeliness and pharmacokinetic features, application of molecular docking studies and simulation. Thus, the present review probably provides AN insight into the role of medicinal chemistry and computational drug discovery to combat Cryptococcus infections and thereby open a new paradigm for the development of novel natural therapeutic against various drug targets for cryptococcal infections.

scholarly journals ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy

2017 ◽  
Author(s):  
Annie Wang ◽  
Hansaim Lim ◽  
Shu-Yuan Cheng ◽  
Lei Xie
Keyword(s):  
Drug Discovery ◽  
Effective Treatment ◽  
Recommender System ◽  
Large Scale ◽  
Single Gene ◽  
Big Data Analytics ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
New Paradigm ◽  
Disease Associations

ABSTRACTExisting1drug discovery process follows a reductionist model of “one-drug-one-gene-one-disease,” which is not adequate to tackle complex diseases that involve multiple malfunctioned genes. The availability of big omics data offers new opportunities to transform the drug discovery process into a new paradigm of systems pharmacology that focuses on designing drugs to target molecular interaction networks instead of a single gene. Here, we develop a reliable multi-rank, multi-layered recommender system, ANTENNA, to mine large-scale chemical genomics and disease association data for the prediction of novel drug-gene-disease associations. ANTENNA integrates a novel tri-factorization based dual-regularized weighted and imputed One Class Collaborative Filtering (OCCF) algorithm, tREMAP, with a statistical framework that is based on Random Walk with Restart and can assess the reliability of a specific prediction. In the benchmark study, tREMAP clearly outperforms the single rank OCCF. We apply ANTENNA to a real-world problem: repurposing old drugs for new clinical indications that have yet had an effective treatment. We discover that FDA-approved drug diazoxide can inhibit multiple kinase genes whose malfunction is responsible for many diseases including cancer, and kill triple negative breast cancer (TNBC) cells effectively at a low concentration (IC50 = 0.87 μM). The TNBC is a deadly disease that currently does not have effective targeted therapies. Our finding demonstrates the power of big data analytics in drug discovery, and has a great potential toward developing a targeted therapy for the effective treatment of TNBC.

ReAction!

2009 ◽  
Author(s):  
Mark A. Griep ◽  
Marjorie L. Mikasen
Keyword(s):  
Drug Discovery ◽  
Chemical Weapons ◽  
Trial And Error ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Special Effects ◽  
Invisible Man ◽  
Feature Films ◽  
The World ◽  
Chemical Companies

ReAction! gives a scientist's and artist's response to the dark and bright sides of chemistry found in 140 films, most of them contemporary Hollywood feature films but also a few documentaries, shorts, silents, and international films. Even though there are some examples of screen chemistry between the actors and of behind-the-scenes special effects, this book is really about the chemistry when it is part of the narrative. It is about the dualities of Dr. Jekyll vs. inventor chemists, the invisible man vs. forensic chemists, chemical weapons vs. classroom chemistry, chemical companies that knowingly pollute the environment vs. altruistic research chemists trying to make the world a better place to live, and, finally, about people who choose to experiment with mind-altering drugs vs. the drug discovery process. Little did Jekyll know when he brought the Hyde formula to his lips that his personality split would provide the central metaphor that would come to describe chemistry in the movies. This book explores the two movie faces of this supposedly neutral science. Watching films with chemical eyes, Dr. Jekyll is recast as a chemist engaged in psychopharmaceutical research but who becomes addicted to his own formula. He is balanced by the often wacky inventor chemists who make their discoveries by trial-and-error.

scholarly journals Partitioning Pattern of Natural Products Based on Molecular Properties Descriptors Representing Drug-Likeness

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 546
Author(s):  
Miroslava Nedyalkova ◽  
Vasil Simeonov
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
De Novo ◽  
Target Prediction ◽  
Natural Compounds ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Unique Source ◽  
The Times ◽  
Partitioning Pattern

A cheminformatics procedure for a partitioning model based on 135 natural compounds including Flavonoids, Saponins, Alkaloids, Terpenes and Triterpenes with drug-like features based on a descriptors pool was developed. The knowledge about the applicability of natural products as a unique source for the development of new candidates towards deadly infectious disease is a contemporary challenge for drug discovery. We propose a partitioning scheme for unveiling drug-likeness candidates with properties that are important for a prompt and efficient drug discovery process. In the present study, the vantage point is about the matching of descriptors to build the partitioning model applied to natural compounds with diversity in structures and complexity of action towards the severe diseases, as the actual SARS-CoV-2 virus. In the times of the de novo design techniques, such tools based on a chemometric and symmetrical effect by the implied descriptors represent another noticeable sign for the power and level of the descriptors applicability in drug discovery in establishing activity and target prediction pipeline for unknown drugs properties.

scholarly journals Genomics-driven drug discovery based on disease-susceptibility genes

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Kyuto Sonehara ◽  
Yukinori Okada
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Drug Repurposing ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

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