Development of QSAR models to predict blood-brain barrier permeability

Faramarzi, Sadegh; Kim, Marlene T.; Volpe, Donna A.; Cross, Kevin P.; Chakravarti, Suman K.; Stavitskaya, Lidiya

doi:10.3389/fphar.2022.1040838

Cited by 8 publications

(8 citation statements)

References 86 publications

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“…Therefore, it is very important to have delivery systems that can overcome this barrier for the treatment of brain‐based diseases such as Alzheimer disease, Parkinson disease and epilepsy. 42 The permissible range of BBB penetration for an optimal drug candidate is 0.73–0.91 43 and, although none of the two candidate drugs (AN‐153I105594 and AN‐153I104845) fall in this range, neither acarbose falls in this range. Hence, we do not expect particular problems of toxicity in this regard.…”

Section: Resultsmentioning

confidence: 99%

Drug screening of α‐amylase inhibitors as candidates for treating diabetes

Alp

Misturini

Sastre

et al. 2023

J Cellular Molecular Medi

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In the present study, the identification of potential α‐amylase inhibitors is explored as a potential strategy for treating type‐2 diabetes mellitus. A computationally driven approach using molecular docking was employed to search for new α‐amylase inhibitors. The interactions of potential drugs with the enzyme's active site were investigated and compared with the contacts established by acarbose (a reference drug for α‐amylase inhibition) in the crystallographic structure 1B2Y. For this active site characterization, both molecular docking and molecular dynamics simulations were performed, and the residues involved in the α‐amylase–acarbose complex were considered to analyse the potential drug's interaction with the enzyme. Two potential α‐amylase inhibitors (AN‐153I105594 and AN‐153I104845) have been selected following this computational strategy. Both compounds established a large number of interactions with key binding site α‐amylase amino acids and obtained a comparable docking score concerning the reference drug (acarbose). Aiming to further analyse candidates' properties, their ADME (absorption, distribution, metabolism, excretion) parameters, druglikeness, organ toxicity, toxicological endpoints and median lethal dose (LD50) were estimated. Overall estimations are promising for both candidates, and in silico toxicity predictions suggest that a low toxicity should be expected.

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Section: Resultsmentioning

confidence: 99%

Drug screening of α‐amylase inhibitors as candidates for treating diabetes

Alp

Misturini

Sastre

et al. 2023

J Cellular Molecular Medi

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“…However, several mechanisms of active transport into and efflux out of the CNS must also be accounted for in these predictions [ 95 ]. Additional data such as in vivo BBB permeability has proven useful for these considerations [ 96 ]. The use of datasets that encompass drug phenotypes including the clinical indication and CNS-related side effects, in addition to physicochemical properties, to determine BBB permeability is a similar approach [ 97 ].…”

Section: Admet Property Predictionmentioning

confidence: 99%

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

2023

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Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

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“…Additionally, a structure-activity relationship (SAR) profiler was constructed through an external collaboration to evaluate potential interactions caused by metabolites. Lastly, DARS recently developed a new model to predict a drug’s potential to cross the blood-brain barrier ( 50 ) and is in the process of developing a model to predict drug-induced cardiotoxicity using post-market safety data.…”

Section: Better Predicting Potential Drug Safety Issuesmentioning

confidence: 99%

New science, drug regulation, and emergent public health issues: The work of FDA’s division of applied regulatory science

et al. 2023

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The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.

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Development of QSAR models to predict blood-brain barrier permeability

Cited by 8 publications

References 86 publications

Drug screening of α‐amylase inhibitors as candidates for treating diabetes

Drug screening of α‐amylase inhibitors as candidates for treating diabetes

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

New science, drug regulation, and emergent public health issues: The work of FDA’s division of applied regulatory science

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