Current Topics in Medicinal Chemistry - Volume 20, Issue 4, 2020

The development of new therapeutics is critically dependent on an understanding of the molecular pathways, the disruption of which results in neurological symptoms. Genetic and biomarker studies have highlighted immune signalling as a pathway that is impaired in patients with neurodevelopmental disorders (NDDs), and several studies on animal models of aberrant neurodevelopment have implicated microglia, the brain’s immune cells, in the pathology of these diseases. Despite the increasing awareness of the role of immune responses and inflammation in the pathophysiology of NDDs, the testing of new drugs rarely considers their effects in microglia. In this brief review, we present evidence of how the study of microglia can be critical for understanding the mechanisms of action of candidate drugs for NDDs and for increasing their therapeutic effect.

Background: The biodegradable and biocompatible nature of pectin-based films is of particular interest in wound dressing applications, due to its non-toxicity, pH-sensitivity and gelling activity. An approach to improve the mechanical properties, the release profile of bioactive compounds as well as the performance in wet environments of pectin-based films is mixing with other biopolymers. Objective: To prepare hydrocolloid films based on crosslinked pectin / starch blend loaded with bioactive extracts from leaves of G. tinctoria and U. molinae with controlled release of bioactive compounds and healing property. Methods: The hydrocolloid films were characterized by FTIR, SEM, and TGA-FTIR techniques and their tensile properties, water uptake, and polyphenolic release profile in aqueous media were evaluated. The dermal anti inflammatory activity of the hydrocolloid films was assessed by the mouse ear inflammation test. The wound healing property of the loaded hydrocolloid films was explored in a rat model and in a clinical trial (sacrum pressure ulcer). Results: The films showed an adequate water-uptake capacity between 100-160%. The release of active compounds from the hydrocolloid films followed the Korsmeyer-Peppas equation. The mechanical properties of hydrocolloid films were not affected by the plant extracts within the concentration range used. The incorporation of the bioactive extracts in the polysaccharide films inhibited the topical edematous response by about 50%. The topical application of the loaded hydrocolloid film on the pressure ulcer is completely closed after 17 days without showing any adverse reaction. Conclusion: A novel hydrocolloid matrix was produced from crosslinked starch-pectin, which exhibited suitable chemical-physical properties to be used as a carrier of plant extracts with wound healing properties.

Background: Staphylococcus aureus is a gram-positive spherical bacterium commonly present in nasal fossae and in the skin of healthy people; however, in high quantities, it can lead to complications that compromise health. The pathologies involved include simple infections, such as folliculitis, acne, and delay in the process of wound healing, as well as serious infections in the CNS, meninges, lung, heart, and other areas. Aim: This research aims to propose a series of molecules derived from 2-naphthoic acid as a bioactive in the fight against S. aureus bacteria through in silico studies using molecular modeling tools. Methods: A virtual screening of analogues was done in consideration of the results that showed activity according to the prediction model performed in the KNIME Analytics Platform 3.6, violations of the Lipinski rule, absorption rate, cytotoxicity risks, energy of binder-receptor interaction through molecular docking, and the stability of the best profile ligands in the active site of the proteins used (PDB ID 4DXD and 4WVG). Results: Seven of the 48 analogues analyzed showed promising results for bactericidal action against S. aureus. Conclusion: It is possible to conclude that ten of the 48 compounds derived from 2-naphthoic acid presented activity based on the prediction model generated, of which seven presented no toxicity and up to one violation to the Lipinski rule.

Aims: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). Background: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). Objective: Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures. Methods: We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied. Results: We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. Conclusion: The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.

Aims: The need to find new antimicrobial agents to cope with this phenomenon increases. Background: Infection diseases are illness caused by different microorganisms, such as bacteria, among those caused by resistant bacteria are associated with greater morbidity, mortality and cost of the treatment than those caused by sensitive bacteria of the same species. Objective: Need to find new antimicrobial agents to cope with this phenomenon increases. Methods: This work carried out the study of biological activities of Cissus incisa, taking account its traditional use. Three extracts were prepared from the leaves of this plant: hexane, chloroform methanol (1:1) and aqueous. Their antibacterial and antitubercular activities were evaluated using microdilution and alamar blue assays; respectively. Results: The chloroform/methanol extract (1:1) was the most active of the three tested extracts for antimicrobial activity. In this way, the extract exhibits a broad spectrum of antimicrobial activity, against the Gram positive and Gram negative bacteria tested, with MIC values between 125 to 500 μg/mL. Conclusion: This research contributes both to the knowledge of the Mexican flora, as well as the discovery of potential antibacterial agents derivate from plants.

Aims: Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. Background: Cheminformatic methods are able to design and create predictive models with high rate of accuracy saving time, costs and animal sacrifice. It has been applied on different disciplines including nanotechnology. Objective: Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. Methods: A systematic study of the regulation and the incorporation of predictive models of biological activity of nanomaterials was carried out through the analysis of the express nanotechnology regulation on foods, applicable in European Union. Results: It is concluded Machine Learning could improve the application of nanotechnology food regulation, especially methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development, European Union regulations and European Food Safety Authority. Conclusion: To our best knowledge this is the first study focused on nanotechnology food regulation and it can help to support technical European Food Safety Authority Opinions for complementary information.