Presented by expert speakers, our webinars will give you advance insight into topics that will be covered at the event.
Can’t make the date? Still register to receive the webinar recording afterwards.
Drug Discovery Webinars
Novel Strategies to Accelerate Discovery of Translatable Therapeutic Targets
Thursday 09 September 2021 | 16:00 BST (UTC+1)
Presented by Zoltan Spiro, Target Discovery Lead at Turbine AI,
Steve Harrison, CSO at Engine Bio & Quin Wills, CSO at Ochre Bio
Moderated by Dave Madge, Vice President, WuXi AppTec Research Service Division
Computers take the lead in oncology: simulation-driven discovery of translatable targets
- How Turbine simulates cancer biology
- The advantages of mechanistic modeling for prioritizing target candidates
- How results are translated into novel, clinically relevant, and druggable target candidates with biomarker hits
Integrating Artificial Intelligence and Combinatorial CRISPR screens to Identify Novel Molecular Targets in Cancer and Neurodegenerative Disease
- How machine-learning based algorithms can be used to perform unbiased surveys of the genome to identify novel disease targets and biomarkers
- The power of combinatorial genetics to validate targets and biomarkers at high throughput
- The use of network biology to better elucidate disease biology and the mechanistic basis for our therapeutic approaches
Deep Phenotyping to Address the Translatability Crisis in Liver Disease R&D
- How throughput biology and machine learning alone are not solving the translatability crisis in chronic liver disease R&D
- Why deep phenotyping technologies such as single-cell and spatial sequencing are so important in studying chronic diseases
- How deep phenotype biology can be done at scale to maximise cost-benefit
Zoltan Spiró PhD
After having finished his Master studies in Biochemistry in Budapest, Zoltan moved to Lausanne, Switzerland, to pursue his PhD at the Swiss Institute for Experimental Cancer Research at EPFL. Thereafter, he was investigating different aspects of biology in Singapore and at IST Austria. In 2018, he switched to applied science by joining the Cancer Pharmacology department of Boehringer Ingelheim in Vienna, where he was coordinating a research team aiming at developing innovative cancer therapeutics using drug-loaded and tumor-targeted extracellular vesicles. Since November 2020, he is working as a Target Discovery Lead for Turbine AI – a start-up company aiming to identify novel cancer targets with the help of computer modeling and artificial intelligence. In his current role, he is leading a team and coordinating in silico target identification screens as well as the execution of target validation studies outsourced to CROs for various target candidates.
Stephen Harrison PhD
Steve joined Engine in May 2018 with over twenty years’ tenure in biotechnology and pharmaceutical discovery and development in the San Francisco Bay Area. A biochemist and molecular biologist, he is highly published and has extensive experience leading product-driven research organizations at all stages, from target identification to early clinical development.
Prior to joining Engine, Stephen was Senior Vice President and CSO at Relypsa (acquired by Galencia for $1.53 billion) where he developed a pipeline of gut-restricted polymer therapeutics targeting systemic diseases. Before that he was Vice President, Research Biology at Nektar Therapeutics (NASDAQ: NKTR), a leader in polymer conjugate therapeutics, where for four years he managed global oncology and pain research efforts. Prior to Nektar, he was Senior Vice President, Research at KAI Pharmaceuticals (acquired by Amgen for $315 million), a company focused on peptide modulators of protein interactions. While at KAI, Stephen generated one development candidate per year and led discovery efforts, including the company’s lead compound for the treatment of secondary hyperparathyroidism, which served as the basis for the company’s eventual acquisition by Amgen. Earlier in his career, Stephen held senior research positions at Chiron Corporation and Thios Pharmaceuticals.
He holds a Ph.D. in Molecular Biology, a M.A. and B.A. in Biochemistry, all from University of Cambridge, England.
Dr. Quin Wills
Quin initially trained as a medical doctor, later reading degrees in genetics, mathematics, computational biology, and a doctorate in systems genomics.
He started his first drug discovery liver genomics company 15 years ago, and over the years he has held numerous leadership positions, from co-steering Oxford University’s single-cell genomics consortium, to establishing the Cellular and Systems Genomics Department for a leading biopharma.
In this role as Head of Genomics, he focused on liver target discovery. However, frustrated by the lack of clinical translation, Quin teamed up with transplant surgeons to study perfused human livers, seeding the idea to co-found an RNA therapeutics company to improve outcomes in subpotimal donor livers.
Dr. Dave Madge
Dave is responsible for the preclinical discovery platform with which WuXi AppTec supports its collaborators in Europe and Israel. With a background in medicinal chemistry, Dave joined WuXi AppTec 7 years ago, after working in biotech (VP Research, Xention), founding NCE Discovery (now Domainex), and supporting an integrated biomedical research group at University College London. Dave started his career at the Wellcome Foundation and received his Ph.D in Chemistry from Imperial College, London.
Dotmatics Platform & Adventures in AI
Thursday 23 September 2021 | 16:00 BST (UTC+1)
Dotmatics software services are used throughout data-driven industries to help research organizations keep on top of their data. Principal Consultant Dan Ormsby PhD will explain how the company is developing artificial intelligence (AI) solutions. Dotmatics customers are already using the platform to query and report on all project data across disparate sources. The next natural step is to use this data for advanced analytics such as AI and ML.
- How customers are partnering with Dan to use their existing data as a training set
- The importance of Feature Engineering in generating interpretable results
- Why you need to embrace AI assistance to stay ahead in R&D
- Practical AI/ML prototypes in the Dotmatics platform
- Automation of AI/ML techniques
- WebAssembly for client side deployment of models