Abstract

The development of brain-penetrant kinase inhibitors remains a major challenge in central nervous system (CNS) oncology and neurodegeneration due to restrictive blood–brain barrier (BBB) transport and efflux mechanisms. We established an AI-enabled, computer-aided drug design (CADD) platform that integrates structure-based modeling, molecular dynamics, kinase selectivity profiling, and pharmacokinetic (PK) optimization to rationally engineer selective, BBB-competent kinase inhibitors with translational potential. Our approach combines orthosteric and allosteric strategies to maximize target engagement while minimizing off-target toxicity and ABC transporter–mediated efflux.

In oncology, we identified highly potent VEGFR2 inhibitors (HA46, HA47; IC₅₀ = 1.3 and 0.72 nM) that significantly suppressed angiogenesis in HUVEC assays and reduced tumor growth in xenograft models without detectable systemic toxicity. Concurrently, selective HER2 inhibitors and dual HER2/VEGFR2-targeted analogs demonstrated robust in vivo efficacy in HER2-positive breast cancer models, with enhanced antitumor activity in combination regimens. BBB permeability was optimized through precise modulation of lipophilicity (LogP/LogD), conformational flexibility, and efflux liability, validated by PAMPA-BBB assays and predictive in silico modeling.

Beyond oncology, repurposed kinase scaffolds exhibited potent antibacterial activity against multidrug-resistant pathogens (MRSA, VRE), underscoring platform versatility. Furthermore, selective modulation of the tau–Fyn signaling axis attenuated Aβ-induced inflammatory responses, supporting therapeutic potential in Alzheimer’s disease.

Collectively, this integrated discovery framework delivers first-in-class, brain-penetrant kinase inhibitors with strong preclinical efficacy, favorable safety profiles, and clear translational trajectories for CNS malignancies, brain metastases, and neurodegenerative disorders.

Speaker Bio

Dr. Hamed I. Ali got his Ph.D. degree in Japan in 2007. He completed his postdoctoral training at TAMU-Rangel College of Pharmacy, then was promoted to Instructor, Lecturer, Assistant Professor, and then Associate Professor. Dr. Ali has several years of expertise in designing, synthesizing, and biologically screening selective tyrosine kinase inhibitors for targeting aggressive metastatic breast cancer. Through his successful scholarly activity, he garnered substantial extramural/intramural funding. He has mentored more than 30 undergraduate, graduate, and Pharm-D students. Accordingly, he received the “Faculty Research Award” in 2023 for demonstrating sustained and impactful scholarship at RCOP. Dr. Ali also demonstrated excellence in teaching Medicinal Chemistry/Pharmacology for Pharm-D and graduate students, being recognized as “Teacher of the Year” in 2017 and 2021 and “Teaching Team of the Year” for 10 consecutive years at RSOP. He received the “Texas A&M University-Distinguished Achievement Award” in 2019.

Abstract

Background: “Pathogenic” or “Likely Pathogenic” mutations in DNA repair genes such as Brca1, Brca2, and Palb2 in the BRCA repair pathway or can lead to an increased risk of developing breast, ovarian, prostate, and pancreatic cancers. Other mutations may be found in the patient population, yet whether they play a role in increasing the risk of developing cancer is unknown; these are called Variants of Undetermined Significance (VUS). The impact of these variants’ expression on cell proliferation, apoptosis, oxidative stress factors, and internalization in certain cell types is unclear. Key DNA repair genes such as BRCA1 and BRCA2 have shown to be important for hematopoiesis in animal models, and some studies have observed differential affects among certain progenitor subsets. Further, several questions remain about the prevalence and impact of DNA repair gene variants in certain tissues. Characterizing the phenotypic impacts of these variants in different cell-based models will provide more information about relevant molecular targets.

Speaker Bio

Victoria Mgbemena is an Associate Professor in the Department of Biology, Marvin D. and June Samuel Brailsford College of Arts and Sciences, Prairie View A&M University. She received a Ph.D. from the University of Texas Health Science Center at San Antonio, where she studied Host-Pathogen Interactions. She completed her Postdoctoral studies in Hematology/Oncology at UT Southwestern Medical Center, where she studied the role of a DNA repair gene in hematopoiesis.

Dr. Mgbemena’s research topics interests include: Inheritance of rare diseases, Reproductive Health, Applications for developing Personalized Medical Approaches, and Preventative Care. She is interested in the following mechanisms: Cell-Cell Communications, modulation of cell metastatic potential by DNA repair pathways.

Abstract

Indole and phenol metabolites- small molecules that come from the breakdown of tryptophan, phenylalanine, and tyrosine- have significant physiological effects. Understanding the sources of these metabolites is crucial for designing effective therapeutic strategies to control their concentrations. Using stable isotope tracing together with gnotobiotic and antibiotic-treated mouse models, we re-examine the prevailing assumption that indole and phenol metabolites originate exclusively from gut microbial metabolism. Our results reveal substantial host contributions to the circulating pools of many of these metabolites.

For phenols that do require gut microbial input, we identify distinct upstream protein sources: host-secreted proteins (such as mucins) are the source for inflammatory phenols, while digestion-resistant dietary proteins are the source for phenols linked with healthy outcomes. We further demonstrate that targeted dietary interventions can selectively alter the circulating concentrations of these phenol classes by reshaping microbial access to their preferred protein substrates. Collectively, these studies highlight the importance of understanding the relative contributions of host and microbial metabolism to physiologically active metabolites.

Speaker Bio

Jenna AbuSalim is an MD/PhD trainee at Rutgers Robert Wood Johnson Medical School Princeton University, where she completed her PhD in Molecular Biology in the lab of Joshua D. Rabinowitz, MD, PhD. Her research in this lab focused on the biochemical interplay between diet, the gut microbiome, and host metabolism. Prior to her doctoral training, she conducted cancer metabolism research as a post-baccalaureate researcher at the National Cancer Institute and Emory University in the lab of Aparna Kesarwala, MD, PhD. She earned her undergraduate degree from Indiana University, completing a Bachelor of Science in Music with an outside field in Chemistry.

Abstract

The seed germination bioassay is widely used to evaluate the phytotoxicity of various chemicals to plants, including fertilizers, manure, herbicides, and plant-derived compounds involved in allelopathy and autotoxicity. Using this assay, we identified phytotoxic effects of chicken manure, but not dairy manure, to collard greens and mustard. However, results can be inconsistent because seeds used for the bioassay are usually selected for availability and rapid germination. For example, our recent study demonstrated that larger seeds tend to be more tolerant to the phytotoxicity of chicken manure than smaller seeds, suggesting that assay outcomes may vary depending on seed type. Consequently, our current research focuses on optimizing the seed germination bioassay by identifying an appropriate model seed to improve test reliability. In this seminar, I will present our progress in applying the seed germination bioassay to assess the phytotoxicity of manure and related chemicals, along with findings that contribute to assay optimization. This research has also proven to be highly effective for undergraduate training, as students who participated in the project gained valuable hands-on research experience and coauthored publications resulting from this work.

Speaker Bio

Tesfamichael Kebrom, PhD. is a research scientist at the Cooperative Agricultural Research Center (College of Agriculture, Food and Natural Resources) and an associate member of the Center for Computational Systems Biology (College of Engineering) at Prairie View A&M University (PVAMU). He received a B.Sc. in Plant Sciences from the University of Asmara in Eritrea, M.Phil. (Master of Philosophy) in Crop Physiology from the University of Reading in England, and a Ph.D. in Molecular & Environmental Plant Sciences from Texas A&M University. His research focuses on identifying the physiological, molecular, and genetic basis of the growth, development, and yield of crop plants and their response to environmental factors.

Abstract

Recent advances in deep learning have revolutionized macromolecular structure prediction, as exemplified by the success of AlphaFold and related frameworks. Despite these breakthroughs, accurately modeling biomolecular assemblies—particularly protein–RNA complexes remains a major challenge, partly due to the scarcity of evolutionary information used as inputs in existing approaches.

In this talk, I will focus on our recent work, ProRNA3D-single, a novel deep learning framework for predicting protein–RNA complex structures. ProRNA3D-single employs geometric attention-enabled pairing of biological language models of proteins and RNAs to predict interatomic interaction maps, which are subsequently transformed into multi-scale geometric restraints for 3D structure modeling. Benchmark results demonstrate that ProRNA3D-single outperforms state-of-the-art methods, including AlphaFold 3, particularly when evolutionary information is limited. I will conclude with a brief overview of my ongoing and future research directions.

Speaker Bio

Rahmatullah Roche, PhD. is a tenure-track Assistant Professor in the Department of Computer Science at Columbus State University. His research focuses on computational biology, applied machine learning, data science, and human-computer interaction, with a particular emphasis on macromolecular predictive modeling using advanced artificial intelligence techniques. Dr. Roche earned his Ph.D. in Computer Science from Virginia Tech in 2024. He holds a Master of Science in Computer Science and Software Engineering from Auburn University (2021) and a Bachelor of Science in Computer Science and Engineering from Bangladesh University of Engineering and Technology (BUET) (2016). He is interested in interdisciplinary collaborations to advance scientific discovery and technological innovation.