Cellworks is a life sciences technology company that improves clinical outcomes and creates value for pharma, payers and physicians. We do this through computational modeling, simulation and annotation of tumor genomics for clinical therapeutics predictions.

Cellworks can,

  • Identify patients who will respond or not respond to a specific treatment

  • Design patient specific cocktails of existing approved agents for unmet treatment needs

  • Identify predictor bio-marker signatures (companion diagnostics) for a drug agent to target clinical trials to the right patients

  • Identify novel target indications for a drug – for drug rescue and repositioning

Cellworks’ approach holistically considers contributions of all disease context genes in its precision medicine predictions, and goes beyond the limited one-gene to one-drug based clinical decision making paradigm.

The current precision medicine paradigm of connecting a single mutation to a clinically actionable decision – for example, patients with ALK mutation being treated with ALK inhibitors or patients with BRCA1/2 mutations being treated with PARP inhibitors – significantly limits the value that can be extracted from innovation.

The SHIVA study confirms the limited value of single gene based precision medicine approach and points to the need for a holistic incorporation of all genes.

We can incorporate multiple gene aberrations, simultaneously, using an adaptive & predictive network simulation model which improves and learns from every study and grows with new science, allowing sharply focused insights into patient specific disease physiology and drug action.

Cellworks’ unique technology allows you to answer, directly, for example, the following kinds of questions:

Case A – You have two patients with the following genomic aberrations (mut – mutation; del – CNV deletion; amp – CNV amplification):
Patient 1 – BRCA2 (del), APC (mut), CARD11 (amp), ADCY1 (amp), HOXA9 (amp), CAMK2B (amp), BAX (del).
Patient 2 – RB1 (mut), MEN1 (mut), TET2 (mut), CREBBP (del), RBBP4 (amp). For a clinical trial of PARP inhibitors which of these patients would you select?

Case B – Will an MDS patient with the following set of genomic aberrations respond to Azacitidine?
TET1 , TET2 , MTHFR, MTRR, TP53, APC, AXIN2 and ASXL1 mutation and a complex cytogenetics profile with loss of various chromosomal segments.

Case C – Which treatment cocktail of approved drugs would an AML patient with the following aberrations respond to?
HER2, FGFR4, IL4R, IL6R, IL7, TET1, DNMT3L are mutated, and various other genes have copy number variations and are amplified (ACLY, NRG1, FGFR1, FGFR3, LYN) or deleted (TET3).

Cellworks consumes disaggregated big data from publications and omics for modeling and interpretation and goes beyond using conventional data transformation based analytics to predict outcomes.

Cellworks’ simulation model is hand crafted, one biological-mathematical relationship at a time, with fastidious attention to the integrity of each incorporated interaction thus eliminating the garbage-in, garbage-out phenomenon that afflicts pure data analytics approaches. The mathematical modeling infrastructure allows customization as ‘per-patient’ omics.

Cellworks has invested over 600 person-years of effort in building its model and our simulation predictions are retrospectively and prospectively validated with biomarker and phenotype trends in lab and clinical settings.

The technology at the heart of this predictive system draws on approaches from semiconductor and IT engineering and combines a graphical representation of human cell networks and the underlying network relationships with drug mechanism-of-action modeling and disease-plus-drug response simulation. Its ability to examine and simulate cross pathway interactions, run huge numbers of complex drug combinations and provide a strong analytical basis for therapies and predictions is unique and immensely powerful.