Venoms and Isolated Toxins from Snakes of Medical Impact in the Northeast Argentina: State of the Art. Potential Pharmacological Applications

South American snake venoms with abundant neurotoxic components. Composition and toxicological properties. A literature review

In South America there are three snake genera with predominantly neurotoxic venoms: Crotalus, Micrurus and Hydrophis, which include nine species/subspecies, 97 species and a single marine species, respectively. Although accidents with neurotoxic venoms are less frequent than those with anticoagulant, cytotoxic or necrotic venoms (e.g. from Bothrops), they are of major public health importance. Venoms from genus Crotalus have been extensively studied, while data on the venoms from the other two genera are very limited, especially for Hydrophis. The venoms of North and South American Crotalus species show biochemical and physiopathological differences. The former species cause bothrops-like envenomation symptoms, while the latter mainly have neurotoxic and myotoxic effects, leading to respiratory paralysis and, occasionally, renal failure by myoglobinuria and death, often with no local lesions. Micrurus and Hydrophis also cause neurotoxic envenomations. Many studies have isolated, identified and characterized new enzymes and toxins, thus expanding the knowledge of snake venom composition. The present review summarizes the currently available information on neurotoxic venoms from South American snakes, with a focus on protein composition and toxicological properties. It also includes some comments concerning potential medical applications of elapid and crotalic toxins.

Fast venomic analysis of Crotalus durissus terrificus from northeastern Argentina

The complete knowledge of the toxins that make up venoms is the base for the treatment of snake accidents victims and the selection of specimens for the preparation of venom pools for antivenom production. In this work, we used a fast and direct venomics approach to identify the toxin families in the C.d. terrificus venom, a Southern American Neotropical rattlesnake. The RP-HPLC separation profile of pooled venom from adult specimens followed by mass spectrometry analysis revealed that C.d. terrificus' venom proteome is composed of 12 protein families, which are unevenly distributed in the venom, e.g., there are few major proteins in the venom's composition phospholipase A₂, serine proteinase, crotamine and L-amino acid oxidase. At the same time, the proteome analysis revealed a small set of proteins with low quantity (less than 1.5%), both enzymes (metaloprotease, phospholipase B and 5'-nucleotidase) and proteins (Bradykinin potentiating and C-type natriuretic peptides, C-type lectin convulxin and nerve growth factor). To sum up, this research is the first venomic report of C.d.terrificus venom from Argentina. This proved to be crotamine positive venom that has a lower metalloprotease content than C.d. terrificus venoms from other regions. This information could be used in the discovery of future pharmacological agents or targets in antivenom therapy.

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.