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Case Studies > Alacris' Innovative Approach to Personalized Cancer Therapy Using Google Cloud Platform

Alacris' Innovative Approach to Personalized Cancer Therapy Using Google Cloud Platform

Technology Category
  • Analytics & Modeling - Digital Twin / Simulation
  • Networks & Connectivity - Cellular
Applicable Industries
  • Healthcare & Hospitals
  • Pharmaceuticals
Use Cases
  • Digital Twin
  • Virtual Reality
Services
  • Cloud Planning, Design & Implementation Services
  • System Integration
The Challenge

Cancer is a complex disease with variations in the genetic makeup of individuals and their tumors, making every patient unique. However, the majority of current medical practice treats many patients identically, leading to wide variations in response to therapy. Typically, only 25% of patients benefit from the often expensive treatment they are given, with many suffering serious side effects. The current approach to cancer drug therapy seems to be largely based on a trial and error principle. Alacris Theranostics, a Berlin-based spin-off company of the Max Planck Institute for Molecular Genetics, sought to overcome this mismatch for patients and healthcare costs using computer models.

About The Customer

Alacris Theranostics is a Berlin-based spin-off company of the Max Planck Institute for Molecular Genetics. The company uses next-generation sequencing and other genomics data through its predictive modeling system ModCell™ for drug development and personalized medicine in oncology. Alacris aims to better match patients to therapies and therapies to patients, recognizing the individuality of each cancer patient and their unique genetic makeup. The company is committed to optimizing personalized cancer therapy and improving the predictive power of virtual clinical trials to test drugs before they go into clinical trials.

The Solution

Alacris developed the ModCell™ system, which uses computer models based on millions of data points to carry out virtual clinical trials and virtual patient modeling. The ModCell™ system generates a 'Virtual Patient' model for individualized prediction of therapy outcome. It first analyses an individual patient and his or her tumor on the molecular level. This patient-specific genetic information is subsequently integrated into a cancer model. Using this model, Alacris can simulate the effects of hundreds of different drugs or drug combinations on the patient tumor right on a computer. The ModCell™ system was used to simulate the effects of about 100 molecular targeted anti-cancer as well as non anti-cancer drugs/compounds on more than 700 different cancer cell lines. Google Cloud Platform enabled Alacris to handle this enormous simulation workload.

Operational Impact

  • The use of Google Cloud Platform has enabled Alacris to rapidly refine and improve their modeling system. The results of the simulations are now being used to calculate the inhibition of cell growth, which will be compared to pharmacological profiles available from Cancer Cell Line Encyclopedia (CCLE). This will potentially facilitate the identification of cancer-related signal transduction pathways that are not yet covered in the model, and also further the understanding of the functional consequences of mutations at both the molecular pathway and cellular level. Moreover, Alacris will gain insight into drug action at the molecular level and analyze cross-talk and potential redundancies between pathways. This information will be used to expand and modify the ModCell™ system, integrating the identified cancer-related signaling pathways and mutations, and additional drug information, such as drug uptake dynamics and drug metabolism.

Quantitative Benefit

  • Simulated the effects of about 100 molecular targeted anti-cancer as well as non anti-cancer drugs/compounds on more than 700 different cancer cell lines.

  • Generated more than 5 million different models, each of which was expected to need up to one minute to be solved numerically and to produce about 500 kB of simulation data.

  • Handled a simulation time of more than 3,000 days, if run on a single core, and more than 2 TB of data using Google Cloud Platform.

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