Resumen:
Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or
diagnostic tools of high therapeutic value. However, only a handful have progressed to the market.
Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity,
the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red
blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold
standard. More recently, many online databases filled with peptide sequences and their biological
meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine
learning-driven programs. This review details the growing contributions of in silico approaches
developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides.
After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the
relevance of early hemolysis studies in drug development. We emphasized the computational models
and algorithms used to this end in light of historical and recent findings in this promising field. We
benchmarked seven predictors using peptides from different data sets, having 7–35 amino acids in
length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal
Matthew’s correlation coefficient over 0.11. The maximum values for these statistical parameters
achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were
described, as well as prospects for future investigations. The development of in silico predictive
approaches to peptide toxicity has just started, but their important contributions clearly demonstrate
their potential for peptide science and computer-aided drug design. Methodology refinement and
increasing use will motivate the timely and accurate in silico identification of selective, non-toxic
peptide therapeutics.
