Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.

The dimeric dihydropyrimidine dehydrogenase (DPD), metalloenzyme, an adjunct anti-cancer drug target, contains highly specialized 4 × Fe²⁺₄S²⁻₄ clusters per chain. These clusters facilitate the catalysis of the rate-limiting step in the pyrimidine degradation pathway through a harmonized electron transfer cascade that triggers a redox catabolic reaction. In the process, the bulk of the administered 5-fluorouracil (5-FU) cancer drug is inactivated, while a small proportion is activated to nucleic acid antimetabolites. The occurrence of missense mutations in DPD protein within the general population, including those of African descent, has adverse toxicity effects due to altered 5-FU metabolism. Thus, deciphering mutation effects on protein structure and function is vital, especially for precision medicine purposes. We previously proposed combining molecular dynamics (MD) and dynamic residue network (DRN) analysis to decipher the molecular mechanisms of missense mutations in other proteins. However, the presence of Fe²⁺₄S²⁻₄ clusters in DPD poses a challenge for such in silico studies. The existing AMBER force field parameters cannot accurately describe the Fe²⁺ center coordination exhibited by this enzyme. Therefore, this study aimed to derive AMBER force field parameters for DPD enzyme Fe²⁺ centers, using the original Seminario method and the collation features Visual Force Field Derivation Toolkit as a supportive approach. All-atom MD simulations were performed to validate the results. Both approaches generated similar force field parameters, which accurately described the human DPD protein Fe²⁺₄S²⁻₄ cluster architecture. This information is crucial and opens new avenues for in silico cancer pharmacogenomics and drug discovery related research on 5-FU drug efficacy and toxicity issues.

The growing opioid crisis and persistent global health disparities underscore the urgent need for alternative therapeutic strategies. Cannabis, with its long-standing historical use and recent legislative developments, presents a potentially significant avenue for addressing these challenges. This review examines the evolving landscape of medicinal cannabis, drawing from historical ethnopharmacological data and contemporary scientific discussions. This review synthesized evidence from historical ethnobotanical records, peer-reviewed studies, and outcomes derived from recent Global Health events. These events facilitated discussions among diverse stakeholders, including healthcare professionals, policymakers, researchers, industry representatives, farmers, and patient advocates. Case studies and successful models from the USA and Africa, including the recent FDA approval for first-in-human clinical trials of a medicinal cannabis derivative for pancreatic cancer, were analysed. Successful models include collaborative platforms fostering stakeholder engagement, such as partnerships between African farmers and biotech firms to standardize cultivation. Successful models demonstrate the potential for adaptation and scaling in diverse healthcare settings. The FDA trial approval exemplifies progress in clinical translation. However, disparities persist, with patients within Africa facing systemic barriers to medical Cannabis certification and higher rates of punitive opioid discontinuation. Medicinal Cannabis presents a complex yet promising solution for reducing opioid-related mortality and addressing healthcare inequities. Implementing strategic frameworks like CORE (Care, Outreach, Research, Education) is essential for developing safe, high-quality, evidence-based cannabis products. Through fostering collaboration and addressing existing obstacles, the therapeutic potential of medicinal Cannabis can be harnessed to mitigate the opioid crisis and reduce global health disparities, effectively translating research from farm to bedside.

The dimeric dihydropyrimidine dehydrogenase (DPD) metalloenzyme, an adjunct anti-cancer drug target contains highly specialized 4 × Fe ²⁺ ₄ S ^2- ₄ clusters per chain. These clusters facilitate the catalysis of the rate-limiting step in the pyrimidine degradation pathway through a harmonized electron transfer cascade that triggers a redox catabolic reaction. In the process, majority of administered 5-fluorouracil (5-FU) cancer drug is inactivated while a small proportion is activated to nucleic acid antimetabolites. The occurrence of missense mutations in DPD protein within the general population, including those of African descent, has adverse toxicity effects due to altered 5-FU metabolism. Thus, deciphering mutation effects on protein structure and function is vital, especially for precision medicine purposes. We previously proposed combined molecular dynamics (MD) and dynamic residue network (DRN) analysis to decipher the molecular mechanisms of missense mutations in other proteins. However, the presence of Fe ²⁺ ₄ S ^2- ₄ clusters in DPD poses a challenge for such in silico studies. The existing AMBER force field parameters cannot accurately describe the Fe ²⁺ center coordination exhibited by this enzyme. Therefore, this study aimed to derive AMBER force field parameters for DPD enzyme Fe ²⁺ centers, using the original Seminario method and collation features Visual Force Field Derivation Toolkit as a supportive approach. All-atom MD simulations were performed to validate the results. Both approaches generated similar force field parameters which accurately described the human DPD protein Fe ²⁺ ₄ S ^2- ₄ clusters architecture. This information is crucial and opens new avenues for in silico cancer pharmacogenomics and drug discovery related research on 5-FU drug efficacy and toxicity issues.

In an era of newly emerging cases of non-communicable diseases such as cancer, research is vital for both the medical and economic well-being of humanity. Pharmacogenomics has laidthegroundworkfor the identification of potential genes in cancer progression and treatment outcome investigations. Researchers are increasingly discovering heterogeneity in the efficacy and toxicity responses of drugmetabolizing enzymes (DMEs) in diverse patient populations receiving anti-cancer therapy. DMEs comprise of Phase I (Cytochrome P450s) and Phase II (glutathione-S-transferases (GSTs), UDP-glucuronosyltransferases (UGTs), and dihydropyrimidine dehydrogenases (DPD)enzymes. The main cause of disparity in DME treatment outcomes is genetic variation,which causes missense mutations leading to structural and kinetic properties of the enzyme. These modifications have a deleterious impact on the pharmacodynamics and pharmacokinetics of drugs through multiple mechanisms. Presently, most cancer medicines are manufacturedin developed countries based on the genetic background of non-African subpopulations. Thus, these drugs may not be optimally effective or can cause adverse side effects. Even though heterogeneity in toxicity and efficacy of these drugs has been observed in African descent, the basis of this population variance remains partially understood. For instance,a deficiencyof DPD, the first-rate limiting metabolizing enzyme in the pyrimidinepathway, causes severe toxicity when exposed to 5-fluorouracil (5-FU) chemotherapy. However, minimum studies have been conducted to unravel itsmolecular mechanismwhich may unravel the observed drug treatment outcomes.The aim of this pharmacogenomics study was to determine the underlying mechanism by which DPD missense mutations, which are associated with an African ancestry subpopulation, provoke dysfunctional 5-FU metabolism, resulting in drug toxicity. This knowledge will be critical in designing drug modulators to aid in the restoration of DPD function, a hallmark of precision medicine. Therefore, in the first part of the research we identified and reviewed the general role of Phase I and Phase II cancer drug metabolizing enzymes. We then used World Health Organization (WHO) essential medicine and drug.com to authenticate the usage of 5-FU as an anti-cancer treatment agent. The 3D structure and chemical structure of the agent was then downloaded from the Drug bank. Subsequently, Human Mutation Analysis - Variant Analysis PORtal (HUMA) and Mendelian Inheritance in Man (OMIM) were used to obtain data on DPD non-synonymous genetic variants. Additionally, the aggregate information of DPD missense mutations and their relation to human health were extracted from ClinVar and Pharmacogenomics Knowledge Base (PharmKGB). This information, along with additional data from single nucleotide polymorphisms (dbSNP), 1000 Genomes Project and Exome Sequencing Project (ESP MAF) considering variants classified based on their minor allele frequency (MAF) of 0.001, as well as research articles, consolidated information on missense mutations associated with African subpopulations. Finally, the wild type (WT) and detected mutation sequences were obtained from the Universal Protein Resources database (UniProt). However, because the 3D structure of human DPD was missing, the dimeric wild type (WT) human 3-dimensional (3D) structure was modeled via MODELLER using the pig’s structure as a template. PRIMO, HHpred, and the Protein Data Bank (PDB) were all used to locate the suitable template. As a result, six clinical (C29R, M166V, Y186C, S534N, I543V, and D949V) and thirteen non-clinical (S201R, K259E, D342N, D432N, S492L, R592Q, A664S, G674D, A721T, V732G, T768K, R886C, and L993R) mutations were discovered. Using AMBER tools, we then determined accurate force field parameters for each monomer of DPD protein's Fe2+ centers. Following the creation of each mutation model structure in Discovery Studio, the resulting AMBER force field parameters were inferred. For each model structure, a drug free (inactive/open-conformation) and drug bound (active/closed-conformation) model structure was created (WT and mutations). The model structures were validated using the consensus of three validation programs, namely ERRAT, PROCHECK, and ProSA. Similarly, the impact on structural functionalities was predicted by consensus from Variant Analysis Porta (VAPOR) web server, which include three support vector machines (SVM)-based tools; PhD-SNP, MUpro, and I-Mutation. After protonation in the H++ web server, the six clinical and thirteen non-clinical (six active site and seven non-active site) mutations identified were then exposed to 600 ns molecular dynamic (MD) simulation. The non-clinical data was divided into two categories to better understand the impact of the mutation based on its position in the protein: six catalytic-domain (R592Q, A664S, G674D, A721T, V732G, and T768K) and seven remote (S201R, K259E, D342N, D432N, S492L, R886C, and L993R) missense mutations. The post-MD analysis was done using the typical existing computational global investigations [RMSD, all versus all RMSD, RMSF, RG, hydrogen bonds (H-bonds) and dynamic cross correlation (DCC)]. In addition, we used in silico tools newly developed within the Research Unit in Bioinformatics (RUBI) group, such as comparative essential dynamics (ED)-principal component analysis and dynamic residue network (DRN) multi-metric [betweenness centrality (BC), closeness centrality (CC), degree of centrality (DC), eigen-centrality (EC) and Katz centrality (KC)] analysis algorithms. From the analysis, it was observed that the loop regions of the mutation proteins had increased loop flexibility, particularly around the catalytic loop, which could account for the enhanced asymmetric behavior of the mutation’s monomers compared to the WT. Notably, the A664S mutant showed relatively lower fluctuations, deviating from the observed heightened...