The World Health Organization (WHO) in Africa and Africa Center Disease Control (Africa CDC) urge the international community and different countries in Africa to ensure sustainable and concrete action to ensure equal and easy access to the COVID‐19 vaccines, as different countries in Africa are still struggling to develop a safe and effective strategy to ensure equal vaccine distribution, if available. Africa CDC has called on the international community to come together to help Africa with COVID‐19 vaccines to make equal the vaccine distribution among African countries as many cannot afford the vaccine costs due to the level of poverty and other negative factors. The African Union has endorsed the need for Africa to develop a framework to actively engage in easy accessibility to COVID‐19 vaccines, which will allow different countries in Africa to take easy steps that will strengthen the local vaccine distribution system, building workforce skills and knowledge, and enrich outreach services in Africa. The article discusses the need for equal access in the distribution of COVID‐19 vaccines in Africa, the challenges, and the necessary recommendations that can help to mitigate these challenges.

Background Colorectal cancer is common to both sexes; third in terms of morbidity and second in terms of mortality, accounting for 10% and 9.2% of cancer cases in men and women globally. Although drugs such as bevacizumab, Camptosar, and cetuximab are being used to manage colorectal cancer, the efficacy of the drugs has been reported to vary from patient to patient. These drugs have also been reported to have varying degrees of side effects; thus, the need for novel drug therapies with better efficacy and lesser side effects. In silico drugs design methods provide a faster and cost-effect method for lead identification and optimization. The aim of this study, therefore, was to design novel imidazol-5-ones via in silico design methods. Results A QSAR model was built using the genetic function algorithm method to model the cytotoxicity of the compounds against the HCT116 colorectal cancer cell line. The built model had statistical parameters; R2 = 0.7397, R2adj = 0.6712, Q2cv = 0.5547, and R2ext. = 0.7202 and revealed the cytotoxic activity of the compounds to be dependent on the molecular descriptors nS, GATS5s, VR1_Dze, ETA_dBetaP, and L3i. These molecular descriptors were poorly correlated (VIF < 4.0) and made unique contributions to the built model. The model was used to design a novel set of derivatives via the ligand-based drug design approach. Compounds e, h, j, and l showed significantly better cytotoxicity (IC50 < 5.0 μM) compared to the template. The interaction of the compounds with the CDK2 enzyme (PDB ID: 6GUE) was investigated via molecular docking study. The compounds were potent inhibitors of the enzyme having binding affinity of range −10.8 to −11.0 kcal/mol and primarily formed hydrogen bond interaction with lysine, aspartic acid, leucine, and histidine amino acid residues of the enzyme. Conclusion The QSAR model built was stable, robust, and had a good predicting ability. Thus, predictions made by the model were reliably employed in further in silico studies. The compounds designed were more active than the template and showed better inhibition of the CDK2 enzyme compared to the standard drugs sorafenib and kenpaullone.

Background: Inhibition activity of the epigenetic readers such as bromodomain and extra-C terminal domain protein family is of high significance in many therapeutic applications due to their ability to regulate gene expression as well as the chromatin structure by binding to acetylysine residues. Objectives: In order to effectively and quickly determine the inhibition activity of these compounds for the desired therapeutic application, this work presents a grid search-based extreme learning machine computational intelligence method through which the inhibition activity of forty different compounds of substituted 4-phenylisoquinolinones was determined. Methods: The prediction and generalization capacity of the developed model were assessed using four different error metrics including root mean square error, mean absolute error, mean absolute percentage deviation and correlation coefficient between the measured values and predicted activities. The lead compound (37), together with kinase inhibitor, LY294002 and a bromodomain and extra-C terminal inhibitor, CPI-0610 were docked with a bromodomain-containing protein 4 bromodomain 1, 6P05. Results: The developed model performed better than the existing model with percentage improvement of 44.48%, 35.08%, 20.44% and 1.23% on the basis of mean absolute percentage deviation, mean absolute error, root mean square error and correlation coefficient, respectively. The lead compound has a better binding score than LY294002 and CPI-0610. Conclusion: Implementation of the developed model would be of immense guide in searching for anti-inflammatory as well as anticancer agents for effective therapeutic application.

Background The number of cancer-related deaths is on the increase, combating this deadly disease has proved difficult owing to resistance and some serious side effects associated with drugs used to combat it. Therefore, scientists continue to probe into the mechanism of action of cancer cells and designing novel drugs that could combat this disease more safely and effectively. Here, we developed a genetic function approximation model to predict the bioactivity of some 2-alkoxyecarbonyl esters and probed into the mode of interaction of these molecules with an epidermal growth factor receptor (3POZ) using the three-dimensional quantitative structure activity relationship (QSAR), extreme learning machine (ELM), and molecular docking techniques. Results The developed QSAR model with predicted (R2pred) of 0.756 showed that the model was fit to be validated parameter for a built model and also proved that the developed model could be used in practical situation, R2 for training set (0.9929) and test set (0.8397) confirmed that the model could successfully predict the activity of new compounds due to its correlation with the experimental activity, the models generated with ELM models showed improved prediction of the activity of the molecules. The lead compounds (22 and 23) had binding energies of −6.327 and −7.232 kcalmol−1 for 22 and 23 respectively and displayed better inhibition at the binding sites of 3POZ when compared with that of the standard drug, chlorambucil (−6.0 kcalmol−1). This could be attributed to the presence of double bonds and the α-ester groups. Conclusion The QSAR and ELM models had good prognostic ability and could be used to predict the bioactivity of novel anti-proliferative drugs.

Background: The continuous increase in mortality of breast cancer and other forms of cancer due to the failure of current drugs, resistance, and associated side effects calls for the development of novel and potent drug candidates. Methods: In this study, we used the QSAR and extreme learning machine models in predicting the bioactivities of some 2-alkoxycarbonylallyl esters as potentials drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydrase target. Results: The QSAR model of choice satisfied the recommended values and was statistically significant. The R2pred (0.6572) was credence to the predictability of the model. The extreme learning machine ELM-Sig model showed excellent performance superiority over other models against MDA-MB-231 breast cancer. Compound 22 with a docking score of 4.67 kcal mol-1 displayed better inhibition of the carbonic anhydrase protein, interacting through its carbonyl bonds. Conclusion: The extreme learning machine’s ELM-Sig model showed excellent performance superiority over other models and should be exploited in the search for novel anticancer drugs.