Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure–activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75–0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78–0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.

Introduction: CD45 is a common marker of leukocytes. Anti-human CD45 monoclonal antibody (MAb) has been used widely in diagnosing and monitoring hematologic diseases. The aim of this study was to generate an anti-human CD45 MAb, which can be used in research and diagnosis. Methods: Recombinant human CD45RO antigen was expressed from E. coli BL21 (DE3), purified and analyzed by SDS-PAGE and Western blotting. The purified CD45RO antigen was used to immunize Balb/c mice. Spleen cells from immunized mouse were collected and fused with P3X63Ag8.653 myeloma cells to form hybridoma. Anti-CD45 antibody-secreting capacity of hybridoma clones was evaluated by ELISA assay. Anti-CD45 MAb from the culture supernatant of the chosen hybridoma clone was purified by affinity chromatography. The MAb was characterized the biochemical characteristics and biological activity. Results: Recombinant human CD45RO antigen was expressed and purified from E.coli BL21 (DE3). Injection of purified CD45RO antigen provoked the immune response in Balb/c mice. Hybridoma clones were generated successfully by the fusion of spleen cells from the selected immunized-mouse and myeloma cells. Among these hybridoma clones, one with the highest yield of MAb production was identified. The isotype of the anti-CD45 MAb created in this work is IgG2b, while its the light chain is kappa (k) type. The affinity of this MAb with CD45RO antigen is high with Kd value at the picomolar level. The anti-CD45 MAb can interact with CD45 naturally expressed on the surface of Jurkat cells in Western blotting and fluorescent immuno-staining assay. Conclusion: We have developed successfully an anti-human CD45 MAb using hybridoma technology, which can recognize CD45 in ELISA, Western blotting, and fluorescent immuno-staining analysis. Although further investigations are necessary, obviously, our anti-human CD45 MAb is potential for research and diagnosis applications.