scholarly journals SELDI ProteinChip®Array Technology: Protein-Based Predictive Medicine and Drug Discovery Applications

2003 ◽  
Vol 2003 (4) ◽  
pp. 237-241 ◽  
Author(s):  
Guru Reddy ◽  
Enrique A. Dalmasso
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Treatment Options ◽  
Drug Effects ◽  
Proteome Analysis ◽  
Predictive Medicine ◽  
Protein Drug ◽  
Proteinchip Array ◽  
Drug Candidates ◽  
Array Technology

Predictive medicine, utilizing the ProteinChip®Array technology, will develop through the implementation of novel biomarkers and multimarker patterns for detecting disease, determining patient prognosis, monitoring drug effects such as efficacy or toxicity, and for defining treatment options. These biomarkers may also serve as novel protein drug candidates or protein drug targets. In addition, the technology can be used for discovering small molecule drugs or for defining their mode of action utilizing protein-based assays. In this review, we describe the following applications of the ProteinChip Array technology: (1) discovery and identification of novel inhibitors of HIV-1 replication, (2) serum and tissue proteome analysis for the discovery and development of novel multimarker clinical assays for prostate, breast, ovarian, and other cancers, and (3) biomarker and drug discovery applications for neurological disorders.

scholarly journals Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search †

Molecules ◽  
2019 ◽  
Vol 24 (12) ◽  
pp. 2233 ◽  
Author(s):  
Michele Montaruli ◽  
Domenico Alberga ◽  
Fulvio Ciriaco ◽  
Daniela Trisciuzzi ◽  
Anna Rita Tondo ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Similarity Search ◽  
Drug Targets ◽  
Search Algorithm ◽  
Confidence Score ◽  
Database Management System ◽  
Therapeutic Drug ◽  
Protein Drug ◽  
Early Protein

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.

scholarly journals Machine Learning Methods in Drug Discovery

Molecules ◽  
2020 ◽  
Vol 25 (22) ◽  
pp. 5277
Author(s):  
Lauv Patel ◽  
Tripti Shukla ◽  
Xiuzhen Huang ◽  
David W. Ussery ◽  
Shanzhi Wang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Drug Candidates ◽  
Novel Drug

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

scholarly journals Learning from the Past: Possible Urgent Prevention and Treatment Options for Severe Acute Respiratory Infections Caused by 2019-nCoV

2020 ◽  
Author(s):  
Jared S. Morse ◽  
Tyler Lalonde ◽  
Shiqing Xu ◽  
Wenshe Liu
Keyword(s):  
Patient Outcomes ◽  
Drug Targets ◽  
Respiratory Infections ◽  
Treatment Options ◽  
Cell Infection ◽  
Drug Candidates ◽  
The Past ◽  
Strong Homology ◽  
Macrocyclic Peptides ◽  
Severe Acute Respiratory Infections

With the current trajectory of the 2019-nCoV outbreak unknown, public health and medicinal measures will both be needed to contain spreading of the virus and to optimize patient outcomes. While little is known about the virus, an examination of the genome sequence shows strong homology with its more well-studied cousin, SARS-CoV. The spike protein used for host cell infection shows key nonsynonymous mutations which may hamper efficacy of previously developed therapeutics but remains a viable target for the development of biologics and macrocyclic peptides. Other key drug targets, including RdRp and 3CLpro, share a strikingly high (>95%) homology to SARS-CoV. Herein, we suggest 4 potential drug candidates (an ACE2-based peptide, remdesivir, 3CLpro-1 and a novel vinylsulfone protease inhibitor) that can be used to treat patients suffering with the 2019-nCoV. We also summarize previous efforts into drugging these targets and hope to help in the development of broad spectrum anti-coronaviral agents for future epidemics.

scholarly journals Natural product drug discovery in the artificial intelligence era

2022 ◽  
Author(s):  
Fernanda I Saldivar-Gonzalez ◽  
Victor Daniel Aldas-Bulos ◽  
José Luis Medina-Franco ◽  
Fabien Plisson
Keyword(s):  
Artificial Intelligence ◽  
Natural Products ◽  
Drug Discovery ◽  
Natural Product ◽  
Drug Targets ◽  
Structural Diversity ◽  
Protein Drug ◽  
Privileged Structures

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even...

scholarly journals Recent Advances in In Silico Target Fishing

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5124 ◽  
Author(s):  
Salvatore Galati ◽  
Miriana Di Stefano ◽  
Elisa Martinelli ◽  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Targets ◽  
Biological Activities ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Specific Protein ◽  
Protein Targets ◽  
Drug Candidates ◽  
Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

scholarly journals Temporal dissociation of phencyclidine: Induced locomotor and social alterations in rats using an automated homecage monitoring system – implications for the 3Rs and preclinical drug discovery

2020 ◽  
Vol 34 (7) ◽  
pp. 709-715 ◽  
Author(s):  
Emma J Mitchell ◽  
Ros R Brett ◽  
J Douglas Armstrong ◽  
Rowland R Sillito ◽  
Judith A Pratt
Keyword(s):  
Drug Discovery ◽  
Radio Frequency Identification ◽  
Time Windows ◽  
Drug Effects ◽  
Data Interpretation ◽  
Nmda Receptor Antagonist ◽  
Drug Candidates ◽  
Environmental Perturbations ◽  
Frequency Identification ◽  
Short Time

Background: Rodent behavioural assays are widely used to delineate the mechanisms of psychiatric disorders and predict the efficacy of drug candidates. Conventional behavioural paradigms are restricted to short time windows and involve transferring animals from the homecage to unfamiliar apparatus which induces stress. Additionally, factors including environmental perturbations, handling and the presence of an experimenter can impact behaviour and confound data interpretation. To improve welfare and reproducibility these issues must be resolved. Automated homecage monitoring offers a more ethologically relevant approach with reduced experimenter bias. Aim: To evaluate the effectiveness of an automated homecage system at detecting locomotor and social alterations induced by phencyclidine (PCP) in group-housed rats. PCP is an N-methyl-D-aspartate (NMDA) receptor antagonist commonly utilised to model aspects of schizophrenia. Methods: Rats housed in groups of three were implanted with radio frequency identification (RFID) tags. Each homecage was placed over a RFID reader baseplate for the automated monitoring of the social and locomotor activity of each individual rat. For all rats, we acquired homecage data for 24 h following administration of both saline and PCP (2.5 mg/kg). Results: PCP resulted in significantly increased distance travelled from 15 to 60 min post injection. Furthermore, PCP significantly enhanced time spent isolated from cage mates and this asociality occured from 60 to 105 min post treatment. Conclusions: Unlike conventional assays, in-cage monitoring captures the temporal duration of drug effects on multiple behaviours in the same group of animals. This approach could benefit psychiatric preclinical drug discovery through improved welfare and increased between-laboratory replicability.

scholarly journals Promising Drug Targets and Compounds with Anti-Toxoplasma gondii Activity

2021 ◽  
Vol 9 (9) ◽  
pp. 1960
Author(s):  
Marco Silva ◽  
Cátia Teixeira ◽  
Paula Gomes ◽  
Margarida Borges
Keyword(s):  
Drug Discovery ◽  
Toxoplasma Gondii ◽  
Drug Targets ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Protozoan Parasite ◽  
Acid Synthesis ◽  
Current Treatment ◽  
Future Research ◽  
World Population

Toxoplasmosis is a parasitic disease caused by the globally distributed protozoan parasite Toxoplasma gondii, which infects around one-third of the world population. This disease may result in serious complications for fetuses, newborns, and immunocompromised individuals. Current treatment options are old, limited, and possess toxic side effects. Long treatment durations are required since the current therapeutic system lacks efficiency against T. gondii tissue cysts, promoting the establishment of latent infection. This review highlights the most promising drug targets involved in anti-T. gondii drug discovery, including the mitochondrial electron transport chain, microneme secretion pathway, type II fatty acid synthesis, DNA synthesis and replication and, DNA expression as well as others. A description of some of the most promising compounds demonstrating antiparasitic activity, developed over the last decade through drug discovery and drug repurposing, is provided as a means of giving new perspectives for future research in this field.

Solid-state NMR spectroscopy as a tool for drug design: from membrane-embedded targets to amyloid fibrils

2007 ◽  
Vol 35 (5) ◽  
pp. 985-990 ◽  
Author(s):  
D.A. Middleton
Keyword(s):  
Drug Discovery ◽  
Solid State ◽  
Solid State Nmr ◽  
Drug Targets ◽  
Amyloid Fibrils ◽  
Enzyme Inhibitors ◽  
Structural Features ◽  
Drug Candidates ◽  
X Ray Crystallography ◽  
Modern Drug

Structure-based design has gained credibility as a valuable component of the modern drug discovery process. The technique of SSNMR (solid-state NMR) promises to be a useful counterpart to the conventional experimental techniques of X-ray crystallography and solution-state NMR for providing structural features of drug targets that can guide medicinal chemistry towards drug candidates. This article highlights some recent SSNMR approaches from our group for identifying active compounds, such as enzyme inhibitors, receptor antagonists and peptide agents, that prevent the aggregation of amyloid proteins involved in neurodegenerative diseases. It is anticipated that the use of SSNMR in drug discovery will become more widespread in the wake of advances in hardware and methodological developments.

scholarly journals Learning from the Past: Possible Urgent Prevention and Treatment Options for Severe Acute Respiratory Infections Caused by 2019-nCoV

2020 ◽  
Author(s):  
Jared S. Morse ◽  
Tyler Lalonde ◽  
Shiqing Xu ◽  
Wenshe Liu
Keyword(s):  
Patient Outcomes ◽  
Drug Targets ◽  
Respiratory Infections ◽  
Treatment Options ◽  
Cell Infection ◽  
Drug Candidates ◽  
The Past ◽  
Strong Homology ◽  
Macrocyclic Peptides ◽  
Severe Acute Respiratory Infections

With the current trajectory of the 2019-nCoV outbreak unknown, public health and medicinal measures will both be needed to contain spreading of the virus and to optimize patient outcomes. While little is known about the virus, an examination of the genome sequence shows strong homology with its more well-studied cousin, SARS-CoV. The spike protein used for host cell infection shows key nonsynonymous mutations which may hamper efficacy of previously developed therapeutics but remains a viable target for the development of biologics and macrocyclic peptides. Other key drug targets, including RdRp and 3CLpro, share a strikingly high (>95%) homology to SARS-CoV. Herein, we suggest 4 potential drug candidates (an ACE2-based peptide, remdesivir, 3CLpro-1 and a novel vinylsulfone protease inhibitor) that can be used to treat patients suffering with the 2019-nCoV. We also summarize previous efforts into drugging these targets and hope to help in the development of broad spectrum anti-coronaviral agents for future epidemics.

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