A Review of the Development of Multitarget Molecules against HIV-TB Coinfection Pathogens.

The human immunodeficiency virus (HIV) produces the pathologic basis of acquired immunodeficiency syndrome (AIDS). An increase in the viral load in the body leads to a decline in the number of T lymphocytes, compromising the patient's immune system. Some opportunistic diseases may result, such as tuberculosis (TB), which is the most common in seropositive patients. Long-term treatment is required for HIV-TB coinfection, and cocktails of drugs for both diseases are used concomitantly. The most challenging aspects of treatment are the occurrence of drug interactions, overlapping toxicity, no adherence to treatment and cases of resistance. Recent approaches have involved using molecules that can act synergistically on two or more distinct targets. The development of multitarget molecules could overcome the disadvantages of the therapies used to treat HIV-TB coinfection. This report is the first review on using molecules with activities against HIV and Mycobacterium tuberculosis (MTB) for molecular hybridization and multitarget strategies. Here, we discuss the importance and development of multiple targets as a means of improving adherence to therapy in cases of the coexistence of these pathologies. In this context, several studies on the development of structural entities to treat HIV-TB simultaneously are discussed.

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors.

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20-25% inhibition of RIPK1's kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.