About

William J. Welsh is a faculty member at the Department of Pharmacology at Robert Wood Johnson Medical School. The department is dedicated to excellence in research, education, and service, with faculty contributing to the training of medical, graduate, and undergraduate students in both classroom and laboratory settings. The department's research spans multiple areas including signaling and gene expression, host-pathogen interactions, genetics, metabolism, liver disease, ion channel pharmacology, cancer biology, translational pharmacology, therapeutic target identification, and drug discovery. Faculty research programs are supported by extramural funding from prominent agencies such as the NIH, Department of Defense, and various foundations, and involve collaboration with other departments and institutes within Rutgers. The faculty are actively involved in graduate student training through Rutgers Biomedical and Health Sciences Graduate Programs, including PhD and master's programs, and are committed to building a diverse and inclusive biomedical research workforce. Recent faculty recruitment has diversified research programs and fostered collaborations with the Center for Advanced Biotechnology and Medicine, the Cancer Institute of New Jersey, and the Child Health Institute. The department emphasizes interdisciplinary teaching and research, with faculty recognized for their contributions to biomedical science and their service on national and international grant review panels, editorial, and advisory boards.

Research topics

• Pharmacology
• Computer Science
• Machine Learning
• Computational biology
• Biochemistry
• Artificial Intelligence
• Medicine
• Biology
• Chemistry
• Bioinformatics
• Internal medicine
• Cancer research

Selected publications

• Preclinical Evaluation of Sigma 1 Receptor Antagonists as a Novel Treatment for Painful Diabetic Neuropathy

  ACS Pharmacology & Translational Science · 2024-07-18 · 9 citations

  articleOpen accessSenior authorCorresponding

  The global prevalence of diabetes is steadily rising, with an estimated 537 million adults affected by diabetes in 2021, projected to reach 783 million by 2045. A severe consequence of diabetes is the development of painful diabetic neuropathy (PDN), afflicting approximately one in every three diabetic patients and significantly compromising their quality of life. Current pharmacotherapies for PDN provide inadequate pain relief for many patients, underscoring the need for novel treatments that are both safe and effective. The Sigma 1 Receptor (S1R) is a ligand-operated chaperone protein that resides at the mitochondria-associated membrane of the endoplasmic reticulum. The S1R has been shown to play crucial roles in regulating cellular processes implicated in pain modulation. This study explores the potential of PW507, a novel S1R antagonist, as a therapeutic candidate for PDN. PW507 exhibited promising in vitro and in vivo properties in terms of ADME, toxicity, pharmacokinetics, and safety. In preclinical rat models of Streptozotocin-induced diabetic neuropathy, PW507 demonstrated significant efficacy in alleviating mechanical allodynia and thermal hyperalgesia following both acute and chronic (2-week) administration, without inducing tolerance and visual evidence of toxicity. To the best of our knowledge, this is the first report to evaluate an S1R antagonist in STZ-induced diabetic rats following both acute and 2-week chronic administration, offering compelling preclinical evidence for the potential use of PW507 as a promising therapeutic option for PDN.

  PublisherOA PDFDOI

• Clinical Care of Patients with Neurocognitive Disorders: A Qualitative Study of the Psychiatric Residency Training Experience

  Academic Psychiatry · 2024-01-30 · 3 citations

  article
  PublisherDOI

• Recent advances in drug discovery efforts targeting the sigma 1 receptor system: Implications for novel medications designed to reduce excessive drug and food seeking

  Addiction Neuroscience · 2023-08-27 · 7 citations

  articleOpen access

  Psychiatric disorders characterized by uncontrolled reward seeking, such as substance use disorders (SUDs), alcohol use disorder (AUD) and some eating disorders, impose a significant burden on individuals and society. Despite their high prevalence and substantial morbidity and mortality rates, treatment options for these disorders remain limited. Over the past two decades, there has been a gradual accumulation of evidence pointing to the sigma-1 receptor (S1R) system as a promising target for therapeutic interventions designed to treat these disorders. S1R is a chaperone protein that resides in the endoplasmic reticulum, but under certain conditions translocates to the plasma membrane. In the brain, S1Rs are expressed in several regions important for reward, and following translocation, they physically associate with several reward-related GPCRs, including dopamine receptors 1 and 2 (D1R and D2R). Psychostimulants, alcohol, as well as palatable foods, all alter expression of S1R in regions important for motivated behavior, and S1R antagonists generally decrease behavioral responses to these rewards. Recent advances in structural modeling have permitted the development of highly-selective S1R antagonists with favorable pharmacokinetic profiles, thus providing a therapeutic avenue for S1R-based medications. Here, we provide an up-to-date overview of work linking S1R with motivated behavior for drugs of abuse and food, as well as evidence supporting the clinical utility of S1R antagonists to reduce their excessive consumption. We also highlight potential challenges associated with targeting the S1R system, including the need for a more comprehensive understanding of the underlying neurobiology and careful consideration of the pharmacological properties of S1R-based drugs.

  PublisherDOI

• Machine learning models to predict ligand binding affinity for the orexin 1 receptor

  Artificial Intelligence Chemistry · 2023-12-20 · 7 citations

  articleOpen access

  The orexin 1 receptor (OX1R) is a G-protein coupled receptor that regulates a variety of physiological processes through interactions with the neuropeptides orexin A and B. Selective OX1R antagonists exhibit therapeutic effects in preclinical models of several behavioral disorders, including drug seeking and overeating. However, currently there are no selective OX1R antagonists approved for clinical use, fueling demand for novel compounds that act at this target. In this study, we meticulously curated a dataset comprising over 1300 OX1R ligands using a stringent filter and criteria cascade. Subsequently, we developed highly predictive quantitative structure-activity relationship (QSAR) models employing the optimized hyper-parameters for the random forest machine learning algorithm and twelve 2D molecular descriptors selected by recursive feature elimination with a 5-fold cross-validation process. The predictive capacity of the QSAR model was further assessed using an external test set and enrichment study, confirming its high predictivity. The practical applicability of our final QSAR model was demonstrated through virtual screening of the DrugBank database. This revealed two FDA-approved drugs (isavuconazole and cabozantinib) as potential OX1R ligands, confirmed by radiolabeled OX1R binding assays. To our best knowledge, this study represents the first report of highly predictive QSAR models on a large comprehensive dataset of diverse OX1R ligands, which should prove useful for the discovery and design of new compounds targeting this receptor.

  PublisherDOI

• QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs

  Molecules · 2021 · 12 citations

  Senior authorCorresponding
  • Computer Science
  • Machine Learning
  • Artificial Intelligence

  S2R overexpression is associated with various forms of cancer as well as both neuropsychiatric disorders (e.g., schizophrenia) and neurodegenerative diseases (Alzheimer's disease: AD). In the present study, three ligand-based methods (QSAR modeling, pharmacophore mapping, and shape-based screening) were implemented to select putative S2R ligands from the DrugBank library comprising 2000+ entries. Four separate optimization algorithms (i.e., stepwise regression, Lasso, genetic algorithm (GA), and a customized extension of GA called GreedGene) were adapted to select descriptors for the QSAR models. The subsequent biological evaluation of selected compounds revealed that three FDA-approved drugs for unrelated therapeutic indications exhibited sub-1 uM binding affinity for S2R. In particular, the antidepressant drug nefazodone elicited a S2R binding affinity Ki = 140 nM. A total of 159 unique S2R ligands were retrieved from 16 publications for model building, validation, and testing. To our best knowledge, the present report represents the first case to develop comprehensive QSAR models sourced by pooling and curating a large assemblage of structurally diverse S2R ligands, which should prove useful for identifying new drug leads and predicting their S2R binding affinity prior to the resource-demanding tasks of chemical synthesis and biological evaluation.

  PublisherOA PDFDOI

• Refinement of Pericardial Fluid Collection and Identification of Unique Immunological Profiles in Pericardial Fluid and Blood serum In VEGF-B Gene Transferred Ischemic and Normoxic Pig Hearts

  UEF eRepo (University of Eastern Finland) · 2021-05-13

  dissertationOpen access1st authorCorresponding
  Publisher

• Cardiovascular Outcomes and the Physical and Chemical Properties of Metal Ions Found in Particulate Matter Air Pollution: a QICAR Study

  UNC Libraries · 2020-11-02 · 3 citations

  articleOpen accessSenior author

  Background: This paper presents an application of quantitative ion character–activity relationships (QICAR) to estimate associations of human cardiovascular (CV) diseases (CVDs) with a set of metal ion properties commonly observed in ambient air pollutants. QICAR has previously been used to predict ecotoxicity of inorganic metal ions based on ion properties.

  PublisherDOI

• Novel Sigma 1 Receptor Antagonists as Potential Therapeutics for Pain Management

  Journal of Medicinal Chemistry · 2020 · 20 citations

  Senior authorCorresponding
  • Chemistry
  • Pharmacology
  • Biochemistry

  exhibited negligible acute toxicity in the rotarod test and statistically significant analgesic effects in the formalin test for acute inflammatory pain and paclitaxel-induced neuropathic pain models during cancer chemotherapy. These encouraging results promote further development of our triazole-based S1R antagonists as novel treatments for pain of different etiologies.

  PublisherOA PDFDOI

• Identification of an irreversible PPARγ antagonist with potent anticancer activity

  Pharmacology Research & Perspectives · 2020 · 13 citations

  Senior authorCorresponding
  • Cancer research
  • Pharmacology
  • Computational biology

  Melanoma is responsible for most skin cancer deaths, and its incidence continues to rise year after year. Different treatment options have been developed for melanoma depending on the stage of the disease. Despite recent advances in immuno- and targeted therapies, advanced melanoma remains incurable and thus an urgent need persists for safe and more effective melanoma therapeutics. In this study, we demonstrate that a novel compound MM902 (3-(3-(bromomethyl)-5-(4-(tert-butyl) phenyl)-1H-1,2,4-triazol-1-yl) phenol) exhibited potent efficacies in inhibiting the growth of different cancer cells, and suppressed tumor growth in a mouse xenograft model of malignant melanoma. Beginning with MM902 instead of specific targets, computational similarity- and docking-based approaches were conducted to search for known anticancer drugs whose structural features match MM902 and whose pharmacological target would accommodate an irreversible inhibitor. Peroxisome proliferator-activated receptor (PPAR) was computationally identified as one of the pharmacological targets and confirmed by in vitro biochemical assays. MM902 was shown to bind to PPARγ in an irreversible mode of action and to function as a selective antagonist for PPARγ over PPARα and PPARδ. It is hoped that MM902 will serve as a valuable research probe to study the functions of PPARγ in tumorigenesis and other pathological processes.

  PublisherOA PDFDOI

• Sigma-1 Receptor Antagonists – Non-Addictive Pain Management

  2019-04-01

  article1st authorCorresponding
  Publisher

Recent grants

• NIH Grant G08LM006230

  NIH · $2.7M · 2007

• NIH Grant R21GM081394

  NIH · $429k · 2011

Frequent coauthors

• Vladyslav Kholodovych

  Rutgers, The State University of New Jersey

  93 shared

• Ni Ai

  Xinjiang University

  70 shared

• Sean Ekins

  Collaborations Pharmaceuticals (United States)

  66 shared

• Kenneth Bachmann

  50 shared

• Michael Sinz

  Biocon (India)

  50 shared

• Sridhar Mani

  Albert Einstein College of Medicine

  50 shared

• Lajos Gera

  49 shared

• J. Gál

  Ben-Gurion University of the Negev

  49 shared

Education

• Postdoctoral Training, Physical Chemistry, Chemistry

  University of Cincinnati

  1983

• Ph.D., Chemistry, Chemistry

  University of Pennsylvania

  1975

• B.S. Chemistry, Chemistry

  St. Joseph's University

  1969