Truvitech, LLC — Department of Health and Human Services STTR Phase I: NCATS

Description

PROJECT SUMMARY Drugs act by altering the activities of particular componentstargetswithin a cell or organismThusdrug discovery campaigns begin by identifying a targetfollowed by screening this target with compounds to identify leads that can be developed into a drugUnfortunatelyidentifying effective drug targets for a given diseaselet alone for individual patientse gin highly heterogeneous cancersis an expensivetime consumingand error prone processAs a resultdrugs are frequently developed against incorrect or suboptimal targetsand end up showing no clinical efficacyPowerful genomic technologies have paved the way for much of the modern understanding of molecular biologybut they have not proven efficient at identifying drug targetsPhenotypic screeningwhich identifies efficacious drugs by screening compounds directly on cellshas thus regained popularityIn phenotypic screeninghoweverthe targets are typically unknownWe are developing an innovative biotechnology platform that directly identifies effective pharmacological targets from cellular disease models by combining the two approachestargetbased and phenotypic based screeningThis is accomplished with the use of a highly annotated chemical library and sophisticated machine learning algorithmsThe compounds are screened in a cell based assayand the phenotypic readouts are analyzed in relation to the compoundsbiochemical activitiesrevealing the candidate targets that are mediating the therapeutic activity of effective compoundsThis approach can one day be applied at the patient levelfor example using patient derived cancer cellsWe have focused our proof of concept studies on the kinase family of drug targetsand hypothesize that our platform can identify kinase dependencies in cancer cells that cannot otherwise be identified using transcriptomic and whole exome sequencing dataThe aims of this Phase I application are todeploy our platform to identify novel kinase targets in DLBCL Lymphomaandidentify kinase inhibitors that could be used to build a compound library that optimizes the performance of the platformInnovative features of the platform include the combination of targetand phenotypic based screeningthe machine learning algorithm that efficiently detects targets as well as anti targetsthe cell based screening strategy which uses both tumor and normal cells to detect cancer specific cytotoxicityand the unique design features of the compound libraryThe platform will enable rapid target identification in any area of disease where a clinically relevant cell based model exists PROJECT NARRATIVE Over the past two decadesthe cost of developing new drugs has skyrocketedA major culprit is the difficulty in identifying cellular components that can be engaged by drugs to produce a therapeutic effectThis proposal has two main aimsThe first aim is to demonstrate that by using computer algorithms to combine biochemicaland cellularscreening dataeffective drug targets can be identified for two different subtypes of DLBCL lymphomaImportantlythe method also identifies off targets thatif disturbedwill counteract the desired outcomei elower or neutralize a drug s efficacyThe method uses normal blood cells from healthy donors to ensure that the identified drug targets serve to specifically abolish cancer cells without harming normal cellsThese key features make our method a valuable complement for currently used target identification technologiesincluding genomics and proteomicsThe second aim is to develop the core components of the methodology into a robust and standalone platform that can be used by drug discovery programs at Truvitechits partnersand its clients!