Road Towards Development of New Antimalarial: Organelle Associated Metabolic Pathways in Plasmodium as Drug Targets and Discovery of Lead Drug Candidates

2019 ◽  
pp. 181-211
Author(s):  
Mohd Asad ◽  
Azhar Muneer ◽  
Pradeep Kumar ◽  
Vandana Thakur ◽  
Sumit Rathore ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Drug Targets ◽  
Drug Candidates

Covid 19 – Structural proteins and prospective drug candidates (Preprint)

2020 ◽  
Author(s):  
A.N Anoopkumar ◽  
Sharrel Rebello ◽  
Embalil Mathachan Aneesh
Keyword(s):  
Causative Agent ◽  
Protein Level ◽  
Drug Targets ◽  
Current Paper ◽  
Structural Proteins ◽  
Drug Candidates ◽  
Propagation Properties

UNSTRUCTURED Covid 19 the causative agent of the current devastating pandemic has turned out to be a notorious virus to all men-irrespective of either common to scientific calibre. Attempts to combat this deadly virus are the need of the hour and quite often the best way to defeat an opponent is to keenly study about its structural and propagation properties. The current paper describes briefly Covid 19 at the genomic, structural and protein level to the best of our knowledge. Furthermore, the prospects of possible drug targets that could aid in the control of this virus are also discussed.

scholarly journals Endoplasmic Reticulum Stress Regulators: New Drug Targets for Parkinson’s Disease

2021 ◽  
pp. 1-10
Author(s):  
Vera Kovaleva ◽  
Mart Saarma
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Endoplasmic Reticulum ◽  
Protein Folding ◽  
Er Stress ◽  
Protein Misfolding ◽  
Drug Targets ◽  
Dopamine Neurons ◽  
Drug Candidates ◽  
The Unfolded Protein Response

Parkinson’s disease (PD) pathology involves progressive degeneration and death of vulnerable dopamine neurons in the substantia nigra. Extensive axonal arborisation and distinct functions make this type of neurons particularly sensitive to homeostatic perturbations, such as protein misfolding and Ca2 + dysregulation. Endoplasmic reticulum (ER) is a cell compartment orchestrating protein synthesis and folding, as well as synthesis of lipids and maintenance of Ca2 +-homeostasis in eukaryotic cells. When misfolded proteins start to accumulate in ER lumen the unfolded protein response (UPR) is activated. UPR is an adaptive signalling machinery aimed at relieving of protein folding load in the ER. When UPR is chronic, it can either boost neurodegeneration and apoptosis or cause neuronal dysfunctions. We have recently discovered that mesencephalic astrocyte-derived neurotrophic factor (MANF) exerts its prosurvival action in dopamine neurons and in animal model of PD through the direct binding to UPR sensor inositol-requiring protein 1 alpha (IRE1α) and attenuation of UPR. In line with this, UPR targeting resulted in neuroprotection and neurorestoration in various preclinical PD animal models. Therefore, growth factors (GFs), possessing both neurorestorative activity and restoration of protein folding capacity are attractive as drug candidates for PD treatment especially their blood-brain barrier penetrating analogs and small molecule mimetics. In this review, we discuss ER stress as a therapeutic target to treat PD; we summarize the existing preclinical data on the regulation of ER stress for PD treatment. In addition, we point out the crucial aspects for successful clinical translation of UPR-regulating GFs and new prospective in GFs-based treatments of PD, focusing on ER stress regulation.

Developing novel chemical entities for the treatment of lysosomal storage disorders: an academic perspective

2015 ◽  
Vol 309 (12) ◽  
pp. F996-F999 ◽  
Author(s):  
James A. Shayman
Keyword(s):  
Drug Targets ◽  
Lysosomal Storage ◽  
Federal Drug Administration ◽  
Drug Candidates ◽  
Gaucher Disease Type 1 ◽  
Biotechnology Companies ◽  
Approved Drugs ◽  
Novel Drug ◽  
Early Drug

Historically, most Federal Drug Administration-approved drugs were the result of “in-house” efforts within large pharmaceutical companies. Over the last two decades, this paradigm has steadily shifted as the drug industry turned to startups, small biotechnology companies, and academia for the identification of novel drug targets and early drug candidates. This strategic pivot has created new opportunities for groups less traditionally associated with the creation of novel therapeutics, including small academic laboratories, for engagement in the drug discovery process. A recent example of the successful development of a drug that had its origins in academia is eliglustat tartrate, an oral agent for Gaucher disease type 1.

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 Translational and Clinical Pharmacology Considerations in Drug Repurposing for X-linked Adrenoleukodystrophy-A Rare Peroxisomal Disorder

2021 ◽  
Author(s):  
Julianne Tieu ◽  
Siddhee Sahasrabudhe ◽  
Paul Orchard ◽  
James Cloyd ◽  
Reena Kartha
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Pharmacokinetics And Pharmacodynamics ◽  
Drug Candidates ◽  
Target Patient Population ◽  
Site Of Action ◽  
Repurposed Drugs ◽  
Gait Disturbances ◽  
Spastic Quadriparesis

X-linked adrenoleukodystrophy (X-ALD) is an inherited, neurodegenerative rare disease that can result in devastating symptoms of blindness, gait disturbances, and spastic quadriparesis due to progressive demyelination. Typically, the disease progresses rapidly, causing death within the first decade of life. With limited treatments available, efforts to determine an effective therapy that can alter disease progression or mitigate symptoms have been undertaken for many years, particularly through drug repurposing. Repurposing has generally been guided through clinical experience and small trials. At this time, none of the drug candidates have been approved for use, which may be due, in part, to the lack of pharmacokinetic/pharmacodynamic (PK/PD) information on the repurposed medications in the target patient population. Greater consideration for the disease pathophysiology, drug pharmacology, and potential drug-target interactions, specifically at the site of action, would improve drug repurposing and facilitate development. Although there is a good understanding of X-ALD pathophysiology, the absence of information on drug targets, pharmacokinetics, and pharmacodynamics hinders the repurposing of drugs for this condition. Incorporating advanced translational and clinical pharmacological approaches in preclinical studies and early stages clinical trials will improve the success of repurposed drugs for X-ALD as well as other rare diseases.

The VPS35-D620N mutation is associated with Parkinson's disease and can be a target for gene therapy

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Metabolic Pathways ◽  
Drug Targets ◽  
Neuronal Loss ◽  
Limited Information ◽  
Tau Pathology ◽  
Therapy Goals ◽  
Axonal Degradation

There is evidence that the VPS35 protein impacts degradation of dopaminergic (DA) neuron lifespan and that the D620N mutation is associated with a kind of Parkinson's disease (PD) mimicking idiopathic PD. The incidence of this mutation and the likely pathogenic effects of additional VPS35 variants is unclear. Other unusual VPS35 mutations may put people at risk for Parkinson's disease, but the level of risk has yet to be determined.Due to the functional and genetic links between VPS35 and other PD-associated genes, rare VPS35 variants may be a key extra component in developing the PD phenotype in people with other mutations with inadequate penetration. Genetic association analysis could remedy this issue in the near future.VPS35-associated PD neuropathology is another significant aspect. Since just one D620N mutant carrier has been studied at autopsy to date, limited information is available about the neuropathological spectrum of PD patients with VPS35 mutations. It is yet unknown if neuronal loss in VPS35-related PD occurs just in SNc or affects other brain areas such as locus coeruleus, cortex, hippocampus and other structures. Neuropathology of VPS35-D620N mice models demonstrated severe tau pathology and axonal degradation, but no evidence of SYN inclusions. It's uncertain if PD individuals with VPS35 mutations have the same features.More study on the role of VPS35 in enhancing DA neuron survival is also needed to better understand the metabolic pathways damaged by VPS35 mutations and identify new therapy goals. The D620N VPS35 KI model, paired with the parkinQ311X mouse model, is one of the first monogenic PD models to recapitulate the fundamental PD feature: DA neuronal breakdown in SNc. These mouse models can be used to identify and assess drug targets. Because the neurodegenerative molecular pathways in many types of Parkinson's disease are so similar, drugs that confer neuroprotection in VPS35 models could be studied in other, more common types of Parkinson's disease.

An insight into anti-arthritic property of phytochemicals against Rheumatoid arthritis using molecular modelling and docking approach

2021 ◽  
Vol 16 (12) ◽  
pp. 185-195
Author(s):  
Bharathi Nathan ◽  
Sudheer M.M. Mohammed
Keyword(s):  
Rheumatoid Arthritis ◽  
Drug Targets ◽  
Joint Inflammation ◽  
Binding Energies ◽  
Multiple Drug ◽  
Matrix Metalloproteinase 3 ◽  
Drug Candidates ◽  
Docking Tool ◽  
Docking Approach ◽  
Interleukin 23

Arthritis literally refers “joint inflammation”, it is a condition where one or more joints are inflamed. More than 100 different types of Arthritis were identified, most common types are rheumatoid arthritis and osteoarthritis. The present study mainly focuses on the development of the novel phytochemical inhibitors against rheumatoid arthritis and osteoarthritis using an integrative cheminformatics drug discovery platform. In this study, we identified potential 405 phytochemical drug candidates, screened against eight selected targets of rheumatoid arthritis and osteoarthritis using molecular docking tool AutoDock. Three phytochemicals Withanolide, Diosgenin and bamyrin exhibited promising binding towards multiple drug targets selected for this study. When comparing with the binding between reference drugs, withanolide showed highest activity against Interleukin-23, Matrix metalloproteinase-3 and Interleukin 8 with binding energies -11.6, -9.4 and -8.3 kcal/mol respectively. Diosgenin also exhibited best activity against three targets that were Interleukin-23, JNK alpha and MMP-3 with -11.3, -10.4, -9.5 kcal/mol binding energies respectively. This study may be important contributing factor to develop new therapeutic drugs for rheumatoid arthritis and osteoarthritis.

scholarly journals Computational Identification of Metabolic Pathways ofPlasmodium falciparumusing thek-Shortest Path Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jelili Oyelade ◽  
Itunuoluwa Isewon ◽  
Olufemi Aromolaran ◽  
Efosa Uwoghiren ◽  
Titilope Dokunmu ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Shortest Path ◽  
Metabolic Pathways ◽  
Drug Targets ◽  
Poor Response ◽  
Metabolic Model ◽  
Pentose Phosphate ◽  
Shortest Path Algorithm ◽  
Alternative Pathways ◽  
Essential Reactions

Plasmodium falciparum, a malaria pathogen, has shown substantial resistance to treatment coupled with poor response to some vaccines thereby requiring urgent, holistic, and broad approach to prevent this endemic disease. Understanding the biology of the malaria parasite has been identified as a vital approach to overcome the threat of malaria. This study is aimed at identifying essential proteins unique to malaria parasites using a reconstructediPfagenome-scale metabolic model (GEM) of the 3D7 strain ofPlasmodium falciparumby filling gaps in the model with nineteen (19) metabolites and twenty-three (23) reactions obtained from the MetaCyc database. Twenty (20) currency metabolites were removed from the network because they have been identified to produce shortcuts that are biologically infeasible. The resulting modifiediPfaGEM was a model using thek-shortest path algorithm to identify possible alternative metabolic pathways in glycolysis and pentose phosphate pathways ofPlasmodium falciparum. Heuristic function was introduced for the optimal performance of the algorithm. To validate the prediction, the essentiality of the reactions in the reconstructed network was evaluated using betweenness centrality measure, which was applied to every reaction within the pathways considered in this study. Thirty-two (32) essential reactions were predicted among which our method validated fourteen (14) enzymes already predicted in the literature. The enzymatic proteins that catalyze these essential reactions were checked for homology with the host genome, and two (2) showed insignificant similarity, making them possible drug targets. In conclusion, the application of the intelligent search technique to the metabolic network ofP. falciparumpredicts potential biologically relevant alternative pathways using graph theory-based approach.

Drugs for Targeted Therapies of Alzheimer’s Disease

2019 ◽  
Vol 26 (2) ◽  
pp. 335-359 ◽  
Author(s):  
Chit Tam ◽  
Jack Ho Wong ◽  
Tzi Bun Ng ◽  
Stephen Kwok Wing Tsui ◽  
Tao Zuo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trial ◽  
Drug Targets ◽  
The Elderly ◽  
Dependent Manner ◽  
Cell Replacement Therapy ◽  
Drug Candidates ◽  
Amyloid A ◽  
Potential Drug Targets

Alzheimer’s disease (AD) is one type of neurodegenerative diseases, which is prevalent in the elderly. Beta-amyloid (Aβ) plaques and phosphorylated tau-induced neurofibrillary tangles are two pathological hallmarks of this disease and the corresponding pathological pathways of these hallmarks are considered as the therapeutic targets. There are many drugs scheduled for pre-clinical and clinical trial that target to inhibit the initiators of pathological Aβ and tau aggregates as well as critical Aβ secretases and kinases in tau hyperphosphorylation. In addition, studies in disease gene variations, and detection of key prognostic effectors in early development are also important for AD control. The discovery of potential drug targets contributed to targeted therapy in a stage-dependent manner, However, there are still some issues that cause concern such as the low bioavailability and low efficacy of candidate drugs from clinical trial reports. Therefore, modification of drug candidates and development of delivery agents are essential and critical. With other medical advancements like cell replacement therapy, there is hope for the cure of Alzheimer’s disease in the foreseeable future.

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.

Sign in / Sign up

Export Citation Format

Share Document