scholarly journals Splicing modulators: on the way from nature to clinic

2021 ◽  
Author(s):  
Tilman Schneider-Poetsch ◽  
Jagat Krishna Chhipi-Shrestha ◽  
Minoru Yoshida
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Chemical Biology ◽  
Mrna Processing ◽  
Mrna Splicing ◽  
Scientific Methodology ◽  
Experimental Drugs ◽  
Structure Activity ◽  
Scientific Tool ◽  
Biology Research

AbstractOver the course of more than two decades, natural products isolated from various microorganisms and plants have built the foundation for chemical biology research into the mechanism of pre-mRNA splicing. Hand in hand with advances in scientific methodology small molecule splicing modulators have become powerful tools for investigating, not just the splicing mechanism, but also the cellular effect of altered mRNA processing. Based on thorough structure-activity studies, synthetic analogues have moved on from scientific tool compounds to experimental drugs. With current advances in drug discovery methodology and new means of attacking targets previously thought undruggable, we can expect further advances in both research and therapeutics based on small molecule splicing modulators.

scholarly journals Tuberculosis Drug Discovery: A Decade of Hit Assessment for Defined Targets

2021 ◽  
Vol 11 ◽  
Author(s):  
Sangmi Oh ◽  
Lena Trifonov ◽  
Veena D. Yadav ◽  
Clifton E. Barry ◽  
Helena I. Boshoff
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Discovery ◽  
Genome Sequence ◽  
Small Molecule ◽  
Mechanism Of Action ◽  
Clinical Testing ◽  
Gene Essentiality ◽  
Structure Activity ◽  
Small Molecule Libraries ◽  
Discovery Pipeline

More than two decades have elapsed since the publication of the first genome sequence of Mycobacterium tuberculosis (Mtb) which, shortly thereafter, enabled methods to determine gene essentiality in the pathogen. Despite this, target-based approaches have not yielded drugs that have progressed to clinical testing. Whole-cell screening followed by elucidation of mechanism of action has to date been the most fruitful approach to progressing inhibitors into the tuberculosis drug discovery pipeline although target-based approaches are gaining momentum. This review discusses scaffolds that have been identified over the last decade from screens of small molecule libraries against Mtb or defined targets where mechanism of action investigation has defined target-hit couples and structure-activity relationship studies have described the pharmacophore.

Quantitative Ranking of Ligand Binding Kinetics with a Multiscale Milestoning Simulation Approach

2018 ◽  
Author(s):  
Benjamin R. Jagger ◽  
Christoper T. Lee ◽  
Rommie Amaro
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Small Molecule ◽  
Brownian Dynamics ◽  
Model Simulation ◽  
Computational Cost ◽  
Lead Selection ◽  
Delta G ◽  
Dynamics Simulations

<p>The ranking of small molecule binders by their kinetic (kon and koff) and thermodynamic (delta G) properties can be a valuable metric for lead selection and optimization in a drug discovery campaign, as these quantities are often indicators of in vivo efficacy. Efficient and accurate predictions of these quantities can aid the in drug discovery effort, acting as a screening step. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict kon’s, koff’s, and G’s. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, -cyclodextrin, based on predicted kon’s and koff’s. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long-timescale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by koff and G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish “best practices” for its future use.</p>

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

Growing by Sharing - Story of Ryvu and Selvita

2020 ◽  
Vol 7 (1) ◽  
pp. 33-47
Author(s):  
Magdalena Marciniak
Keyword(s):  
Drug Discovery ◽  
Business Model ◽  
Small Molecule ◽  
Contract Research ◽  
Industrial Enterprises ◽  
Strong Position ◽  
Small Molecule Therapeutics

Ryvu Therapeutics and Selvita originated in 2007, a time when drug discovery in Poland was still not pursued by industrial enterprises. For many years, both entities operated one company and were known under a common name Selvita S.A., combining their efforts on both innovative small-molecule therapeutics for oncology and expertise in Contract Research Services (CRO). Following more than a decade of such a hybrid business model, Selvita established a strong position in the field of drug discovery and built trust among partners, clients, and investors globally. This encouraged the leaders of the company to separate the two divisions into fully autonomous units, which in fact, had already been operating quite independently and both were successful in diverse areas of drug discovery activities. At the beginning of October 2019, two new companies were established and both parts were given independence and more opportunities for growth. Discovery and development engine was named as Ryvu Therapeutics, and the CRO part of the company remained with the name Selvita. To reach this stage, both the divisions went through an interesting journey together, supporting and strengthening each other for the benefit of both.

Targeted Protein Degradation: "The Gold Rush is On!"

2020 ◽  
Vol 7 (1) ◽  
pp. 4-16
Author(s):  
Daria Kotlarek ◽  
Agata Pawlik ◽  
Maria Sagan ◽  
Marta Sowała ◽  
Alina Zawiślak-Architek ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Degradation ◽  
Small Molecule ◽  
Gold Rush ◽  
European Company ◽  
Drug Candidates ◽  
Protein Inhibition ◽  
Small Molecule Drug ◽  
Therapeutic Benefits ◽  
Talent Pool

Targeted Protein Degradation (TPD) is an emerging new modality of drug discovery that offers unprecedented therapeutic benefits over traditional protein inhibition. Most importantly, TPD unlocks the untapped pool of the proteome that to date has been considered undruggable. Captor Therapeutics (Captor) is the fourth global, and first European, company that develops small molecule drug candidates based on the principles of targeted protein degradation. Captor is located in Basel, Switzerland and Wroclaw, Poland and exploits the best opportunities of the two sites – experience and non-dilutive European grants, and talent pool, respectively. Through over $38 M of funding, Captor has been active in three areas of TPD: molecular glues, bi-specific degraders and direct degraders, ObteronsTM.

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