Drug development panel discussed how the technological advancements in Big Data, Machine Learning and Cloud Computing, when paired with focus on combination therapies that include for instance, anti cancer and immuno oncology agents, or paired with next gen sequencing technologies, and advanced companion diagnostics, with relentless focus on patient outcomes, may open new frontiers in cost effective drug development.
Drug development panel was moderated by Dr. Suneel Gupta, Chief Scientific Officer at Impax Pharmaceuticals. Gupta has over 25 years experience in pharmaceutical R&D, specifically around drug delivery technologies. Gupta shared the story of how Impax launched RYTARY, an extended-release oral capsule formulation drug, for the treatment of Parkinson’s disease, and took it in a span of 3.5 years, with $100M, from benchmark to launch. This is Impax’s first branded drug internally developed and approved for commercialization and it was achieved with “relentless execution”, said Gupta. Gupta’s advice to the entrepreneurs was to focus on the patient, not technology; to focus on what the product does, not what the product is made of. He also advised to keep the the focus on the effect and go for big effect size, to get the drug approved faster.
Brandon Allgood, CTO at Numerate began by talking about the challenges of existing computational methods which so far have been strictly dependent on either high resolution crystal structure data or very clean SAR screening data (QSAR). These models did not work well where computing is most needed, with disparate data, with emerging targets, and in high content, low throughput biology, and for multi target optimizations, said Allgood. However, with a vast number of technological advancements in cloud computing, big data, and machine learning algorithms, Numerate has overcome major challenges in drug discovery, said Allgood.
Numerate has created a powerful drug design platform that can rapidly deliver novel leads on targets, without the need for a crystal structure and with very limited SAR data. It can be used with just some ligand data and that is perfect for emerging targets, said Allgood. Numerate’s machine learning algorithms can integrate and make predictions from small amounts of public data available from patents, literature review etc. and make accurate predictions and give google or netflix type ranking to data design. It can handle noise and bias and give tolerance windows to address inter lab measurement variance. Numerate has built 2100 off target models and also has advanced ADME models, said Allgood. Speaking of some of the challenges in this area, Allgood said, public data is noisy and biased, while private data is private. He suggested following changes. 1) To spur innovation, big pharma should be encouraged to release data so others can apply machine learning algorithms to more data. 2) There is a need to put in place some standards for machine learning to standardize lab validations. 3) While genomics has received a bulk of funding, there is need to put investment behind research on small molecule drugs; “they still have a future”, said Allgood.
Dirk Brockstedt, SVP of R&D at Aduro BioTech, began by saying 2013 brought in a new era in cancer immunotherapy with approvals for Yervoy, Provenge, Opdivo, and Keytruda. The opportunity exists for rethinking about the biology and cancer treatment and look for combination of anti cancer and immuno oncology agents that can move the cure to the right for an increasing proportion of cancers, said Brockstedt. The key is to target the immune and not the cancer cell in developing innovative therapies. We need to also develop new clinical endpoints, develop new trial designs with new statistical methods, and consider novel regulatory paths for accelerated approvals of combination therapies. When only a subset of patients respond well, we need to apply novel technologies and methods for patient identification and stratification, said Brockstedt.
Brockstedt talked about Aduro Biotech’s novel approach for tackling the disease, with listeria bacteria. Here is my previous blog on Aduro’s approach http://bit.ly/JqDJ3K and here’s link to recently aired Scott Pelly’s segment on 60 minutes, on the use of polio bacteria for treatment of glioblastoma http://tinyurl.com/pkspcmz . These potential therapies are in early stage and it remains to be seen how successful the genetic engineering will be to render them useful as cancer drugs.
Eric Peters, Group Leader of Companion Diagnostics at Genentech discussed the challenge of expediting drug discovery and development through the use of next gen sequencing technology. Currently the cost to bring a new drug to patients (including failures) is around $2.6 B. There is only 12% success rate in drug development. However, also the knowledge of disease heterogeneity is rapidly evolving. Now we speak not of lung cancer but lung cancers, said Peters. There is a need for large scale biomarker and phenotype datasets. Access to high quality data from multiple sources is the most essential element. Patients’ access to complete range of testing and comprehensive diagnostics will play a big role in the future, and will become a standard of care in the future, said Peters.
Here are some additional blog links for your convenience
EPPICon 2015 keynote by Vivek Wadhwa – http://bit.ly/1abPwr5
“EPPICon 2015 Digital Health Panel Preview” http://bit.ly/1EQtd5y
“EPPICon 2015 Keynote by Kim Bush on “Tackling Global Health at Gates Foundation” http://bit.ly/18SV1cx
Feel free to browse my blog for past EPPIC conferences and other articles.