Among all routes of drug delivery, oral is the most preferred route because oral formulations are less expensive and less prone to infection caused by inappropriate use/reuse of needles.

In addition, alternate routes such as pulmonary, oral, intranasal, buccal, transdermal, ocular and rectal have been tested. Presently, most of the therapeutic peptides are used in the form of injection via subcutaneous, intravenous/intramuscular route. Also, a number of computation tools such as CellPPD, TumorHPD, AntiCP, AVPpred and ToxinPred have been developed to predict and design cell penetrating, tumor homing, anticancer, antiviral and toxic peptides, respectively. Owing to their immense therapeutic importance, peptides have been curated from literature and stored in form of databases such as Hemolytik, PhytAMP, APD2, DAMPD, CAMP, YADAMP, CPPsite, TumorHoPe, Quorumpeps, Brainpeps, MilkAMP, DADP, LAMP and AVPdb to name a few. A large number of peptides like insulin, cyclosporine, corticotropin are used for treating various diseases like diabetes and immunoregulatory disorders. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route ( ).Дue to rapid advancement in peptide and peptidomimetic techniques, pharmaceutical companies are focusing towards peptides-based therapeutics. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. In summary, this study describes a web server вЂHLPвЂ™ that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. The web server provides three facilities i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides. Based on above models, we have developed a web server named HLP ( Half Life Prediction), for predicting and designing peptides with desired half-life. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Secondly, models developed on HL16 dataset showed maximum R/R 2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R 2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. In this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials.

Server de half life