Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced “omics” coupled with machine learning and artificial intelligence ...(deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.
While establishing efficacy in translational models and humans through clinically-relevant endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug ...is critical. Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions that mediate known adverse events or adverse outcome pathways (AOPs).
We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based on the writers' expertise and intent in communicating important aspects of in silico toxicology to the interested reader.
This review provides a purview of computational methods of pre-clinical toxicologic assessments for novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and commercial drug discovery entities.
Antibiotic resistance and treatment adherence remain significant challenges for acne treatment. Advances in topical formulations have ushered in an era of fixed combination topical therapeutics that ...are well-tolerated and more efficacious. In addition, their once-daily application leads to increased treatment adherence. This article discusses formulation strategies that allow for the coadministration of active drugs and reviews all commercially available fixed-combination topical acne treatments. J Drugs Dermatol. 2024;23:4(Suppl 2):s4-10.
Plaque psoriasis is a chronic, immune-mediated, cutaneous, and systemic inflammatory dermatosis. Its pathogenesis involves the dysregulation of the interleukin (IL)-23/IL-17 signaling pathway. There ...are a range of treatment options available, encompassing topical agents, biologics, oral systemic therapy, and phototherapy. The utility of combination treatment has also been described and is a budding field of research. Here we describe the first case of adult severe generalized plaque psoriasis treated with once-daily oral deucravacitinib 6 mg combined with tapinarof cream 1% applied once daily. To our knowledge, the combination of these agents has not yet been described in the literature. J Drugs Dermatol. 2024;23(3): doi:10.36849/JDD.8091.
Prurigo nodularis (PN) is a quintessential neurocutaneous condition characterized by neural sensitization and intractable itch leading to intense scratching. This causes the formation of nodules with ...epidermal thickening and further release of pro-inflammatory mediators that recruit immune cells and increase dermal nerve proliferation and hypertrophy perpetuating the itch-scratch cycle. Those with PN have a significant quality-of-life (QoL) burden due to itch, anxiety, and sleep disturbance. In addition, PN exhibits psychiatric comorbidities that affect mental wellbeing such as depression, mood disorders, and substance abuse. This paper serves as an overview of the clinicopathologic aspects of PN, the burden of PN on QoL, and the psychodermatological aspects of the disease state. J Drugs Dermatol. 2023;22:12(Suppl 2):s6-11.
Cosmetic procedures for antiaging carry inherent risks of adverse events. One that has not yet been well characterized is transitory or permanent alopecia. This is attributable to numerous mechanisms ...including pressure, ischemia, inflammation, and necrosis. Cases of postcosmetic procedure alopecia have been reported after mesotherapy as well as hyaluronic acid filler, deoxycholic acid, and botulinum toxin injections.
This review serves to describe the currently known causes of postcosmetic procedure alopecia and the mechanisms by which alopecia is attained. Furthermore, this review highlights the risk of unregulated mesotherapy injections for cosmetic enhancement and to bring attention to the increasing number reports of alopecia after these procedures.
A systematic review of the literature from 2000 to 2022 was conducted looking for keywords such as "alopecia," "cosmetic procedures," "mesotherapy," and "hyaluronic acid" in Google Scholar and PubMed.
Ten articles met the criteria set forth in the authors' literature review. Many of the procedures resulted in partial or complete resolution of alopecia.
Alopecia after cosmetic injection procedures is an underreported adverse effect. More research is needed to further characterize the risk of alopecia after mesotherapy and other injection procedures.
Electrostatic interactions drive biomolecular interactions and associations. Computational modeling of electrostatics in biomolecular systems, such as protein-ligand, protein-protein, and ...protein-DNA, has provided atomistic insights into the binding process. In drug discovery, finding biologically plausible ligand-protein target interactions is challenging as current virtual screening and adjuvant techniques such as docking methods do not provide optimal treatment of electrostatic interactions. This study describes a novel electrostatics-driven virtual screening method called 'ES-Screen' that performs well across diverse protein target systems. ES-Screen provides a unique treatment of electrostatic interaction energies independent of total electrostatic free energy, typically employed by current software. Importantly, ES-Screen uses initial ligand pose input obtained from a receptor-based pharmacophore, thus independent of molecular docking. ES-Screen integrates individual polar and nonpolar replacement energies, which are the energy costs of replacing the cognate ligand for a target with a query ligand from the screening. This uniquely optimizes thermodynamic stability in electrostatic and nonpolar interactions relative to an experimentally determined stable binding state. ES-Screen also integrates chemometrics through shape and other physicochemical properties to prioritize query ligands with the greatest physicochemical similarities to the cognate ligand. The applicability of ES-Screen is demonstrated with in vitro experiments by identifying novel targets for many drugs. The present version includes a combination of many other descriptor components that, in a future version, will be purely based on electrostatics. Therefore, ES-Screen is a first-in-class unique electrostatics-driven virtual screening method with a unique implementation of replacement electrostatic interaction energies with broad applicability in drug discovery.
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