Tomohiro Andou graduated from the Graduate School of Pharmaceutical Science, the University of Tokyo in 2009. He is engaged in pharmacokinetics and biomarker research at Takeda Pharmaceutical Company Limited, He assumed his current position in 2017. (Integrated & Translational Science, Omics, Principal Researcher, Doctor of pharmacology)
- Drug discovery research, shifting from researcher-driven to data-driven?!
- Finding a way for research with no measures for moving forward
- Passion for leading-edge techniques and data quality that is necessary for drug discovery research
- Omics analysis breaks down situations that block the way for drug development —Aiming for clinical application—
1Drug discovery research, shifting from researcher-driven to data-driven?!
First, and this is an amateur question, what is an omics analysis?
An omics analysis is a comprehensive investigation of biomolecules. An analysis for proteins is called proteomics, for metabolites it’s called metabolomics, and for lipids it’s called lipidomics, and my group is working on these three types of analysis using a mass spectrometer.
How can the omics analysis help mechanism exploration?
For example, there is a compound that acts on a certain target. This compound exerts drug effect by regulating protein activity to change metabolites. In this case, because the expression levels of mRNA and proteins are not changed, the true mechanism cannot be revealed without a comprehensive metabolite analysis, i.e., metabolomics. When thinking about potential pharmacodynamic mechanisms, focusing only on mRNA, proteins, or metabolites may lead to oversights, which is why using metabolomics and lipidomics for the exploration becomes important. Only comprehensive data that do not depend on researchers and is able to capture the whole picture can be helpful.
Comprehensive data collection with next-generation sequencing and mass spectrometry technologies makes it possible to obtain the whole picture of mRNA, proteins, and metabolites, which are important in understanding life phenomena, from a limited amount of samples in a single analysis, leading to significant time-saving and cost reduction in experiments. In addition, by cross-referencing these data, integrated mechanism exploration, with less oversight and higher reliability, becomes possible. The analyses of metabolome and proteome are called metabolomics and proteomics, respectively.
It does not depend on researchers?!
But, for handling the massive amounts of data obtained,
don’t the extraction and interpretation of data largely depend on researchers?
Omics research is not the kind of research that just anyone can start from today or tomorrow. Also, the collected data is very complicated, and setting up analysis systems is not easy. However, because the tools that process a vast amount of data and objectively interpret them have been increasing recently, after choosing the appropriate tool, we can eliminate a researcher’s subjectivity as much as possible and let the tool interpret the data. Of course, the capability of researchers is important, but the knowledge of each researcher has limitations. Therefore, if we made hypotheses under such circumstances, we may miss something and fail to find an answer. I believe that the key to improving the precision of mechanism exploration is data-driven research using objective interpretation of comprehensive data without depending on individual knowledge.
I see. So, in mechanism exploration, do you generally use proteomics, metabolomics, and lipidomics all as a set?
In some cases, doing all three of them is ideal, but it will cost a lot. So, this is where researchers can show their skills. I always keep in mind to make a proposal that can reduce costs and cover everything while thinking about which analysis is better for the client’s hypothesis.
Do you mean that you will be the pilot who chooses which comprehensive data to use and the tools for interpretation to make a whole plan?
Yes. Our advantage is that we are drug discovery researchers with work experience in pharmaceutical companies. We understand what kind of challenges specifically drug discovery projects have. And we know which tools are really useful among day-to-day evolving tools, and which tools are the best for our research data, because we have experienced many analyses in actual projects. Unfortunately, we cannot aim to be Nobel Prize winners with omics research. Instead, we will compete with our long experience in drug discovery research.
2Finding a way for research with no measures for moving forward
Tell me about an exemplary project in which omics research contributed to drug discovery research.
There was a doctor who had been researching a certain genetic disorder. We proposed lipidome and metabolome analyses to the doctor, and conducted experiments in the murine model. As a result, a hypothesis of an effective therapy was successfully established. This research had been continuing for about 20 years with almost no involvement of omics research. Then, the omics approach was brought in, and it could show the whole picture of the disorder, resulting in the discovery of a therapy.
Wow, the research had gone nowhere for 20 years and could make progress in only a few months!
I assume, as is true with any research, the key is to do what no one else does. Omics is a powerful tool, particularly when there are no measures for moving forward. If the research has run into a brick wall, or a suspended project is expected to be restarted, I recommend considering omics, which can possibly show a new way to clients.
How can omics be helpful in ongoing projects?
In the period between the creation of lead compounds and the optimizing stage, what is required the most is evidence that indicates that the concept is right. If there are no data suggesting possible effectiveness in humans, companies cannot invest the enormous costs required for research and development to proceed to the clinical stage. To clear this issue, the validation of treatment hypotheses by performing mechanism exploration in non-clinical models can provide important information for making a decision.
In addition, when considering out-licensing, a concrete explanation, such as, “the drug is effective with this mechanism”, makes a client’s plan more appealing to potential in-licensing companies and investors. Mechanism exploration will play a major role in enhancing the additional value of candidate compounds for development.
|Target identification to Lead compound creation||Elucidating the pharmacodynamic mechanism of action of seed compounds|
|Lead compound creation to Lead compound optimization||Verifying treatment hypotheses in nonclinical models|
|Lead compound optimization to IND application study||Elucidating the mechanism of toxicity|