scholarly journals Re-Envisioning Anti-Apicomplexan Parasite Drug Discovery Approaches

2021 ◽  
Vol 11 ◽  
Author(s):  
Gabriel W. Rangel ◽  
Manuel Llinás
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Large Scale ◽  
Environmental Changes ◽  
Rapid Evolution ◽  
Antimalarial Drugs ◽  
Life Cycles ◽  
Drug Pressure ◽  
Apicomplexan Parasites ◽  
Resistance Evolution

Parasites of the phylum Apicomplexa impact humans in nearly all parts of the world, causing diseases including to toxoplasmosis, cryptosporidiosis, babesiosis, and malaria. Apicomplexan parasites have complex life cycles comprised of one or more stages characterized by rapid replication and biomass amplification, which enables accelerated evolutionary adaptation to environmental changes, including to drug pressure. The emergence of drug resistant pathogens is a major looming and/or active threat for current frontline chemotherapies, especially for widely used antimalarial drugs. In fact, resistant parasites have been reported against all modern antimalarial drugs within 15 years of clinical introduction, including the current frontline artemisinin-based combination therapies. Chemotherapeutics are a major tool in the public health arsenal for combatting the onset and spread of apicomplexan diseases. All currently approved antimalarial drugs have been discovered either through chemical modification of natural products or through large-scale screening of chemical libraries for parasite death phenotypes, and so far, none have been developed through a gene-to-drug pipeline. However, the limited duration of efficacy of these drugs in the field underscores the need for new and innovative approaches to discover drugs that can counter rapid resistance evolution. This review details both historical and current antimalarial drug discovery approaches. We also highlight new strategies that may be employed to discover resistance-resistant drug targets and chemotherapies in order to circumvent the rapid evolution of resistance in apicomplexan parasites.

scholarly journals Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 23
Author(s):  
Gajinder Pal Singh
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Large Scale ◽  
Sequence Similarity ◽  
Artemisinin Resistance ◽  
Essential Genes ◽  
Gene Essentiality ◽  
Apicomplexan Parasites ◽  
Functional Classes ◽  
Human Proteins

New anti-malarial drugs are needed to address the challenge of artemisinin resistance and to achieve malaria elimination and eradication. Target-based screening of inhibitors is a major approach for drug discovery, but its application to malaria has been limited by the availability of few validated drug targets in Plasmodium. Here we utilize the recently available large-scale gene essentiality data in Plasmodium berghei and a related apicomplexan pathogen, Toxoplasma gondii, to identify potential anti-malarial drug targets. We find significant conservation of gene essentiality in the two apicomplexan parasites. The conservation of essentiality could be used to prioritize enzymes that are essential across the two parasites and show no or low sequence similarity to human proteins. Novel essential genes in Plasmodium could be predicted based on their essentiality in T. gondii. Essential genes in Plasmodium showed higher expression, evolutionary conservation and association with specific functional classes. We expect that the availability of a large number of novel potential drug targets would significantly accelerate anti-malarial drug discovery.

Multiple-source Record Linkage in a Rural Industrial Community, 1680–1820

1994 ◽  
Vol 6 (3) ◽  
pp. 133-142 ◽  
Author(s):  
Steve King
Keyword(s):  
Early Modern ◽  
Large Scale ◽  
Social Economic ◽  
Life Cycles ◽  
Multiple Source ◽  
Demographic Analysis ◽  
Ordinary People ◽  
Industrial Community ◽  
The Social ◽  
Broad Outline

Re-creating the social, economic and demographic life-cycles of ordinary people is one way in which historians might engage with the complex continuities and changes which underlay the development of early modern communities. Little, however, has been written on the ways in which historians might deploy computers, rather than card indexes, to the task of identifying such life cycles from the jumble of the sources generated by local and national administration. This article suggests that multiple-source linkage is central to historical and demographic analysis, and reviews, in broad outline, some of the procedures adopted in a study which aims at large scale life cycle reconstruction.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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