Astellas Pharma Inc. Recruit

Such as design a drug discovery hypothesis, verification of target validity, selection of the optimal therapeutic method (modality) for the target, and pharmacological characterization of the newly developed candidate are critical steps in the drug discovery process.
Through pharmacology research based on biology and science, we will contribute to the generation of products by establishing a target product profile (TPP) for the candidate, evaluating drug efficacy using pathological models including human tissues, and analyzing correlations with biomarkers.

Translational research is research that links exploratory research to clinical development. It contributes to early Go/No-Go decisions on the conduct of clinical studies, development of precision medicine, and reliable proof-of-concept (POC). By exploring and identifying biomarkers for drug efficacy and patient stratification using cutting-edge technologies (omics, new high-sensitivity assays, pathology analysis, etc.), developing measurement methods and diagnostic agents, and by measuring biomarkers, it contributes to the estimation of optimal clinical doses for development candidates and the prediction of eligible patient populations. In addition, by making use of various bioimaging technologies widely used in clinical diagnosis such as positron emission tomography (PET), magnetic resonance imaging (MRI), and X-ray computed tomography (CT), we contribute to the promotion of research by enabling the visualization and quantification of pharmacological and pharmaceutical properties of clinical development candidates in exploratory research.

Modality is the material type of the therapeutic pharmaceutical product, such as small molecule compounds, antibodies, cell therapies, gene therapies, and regenerative medicine.
We are challenging new technologies such as bacteriophage and mRNA, in addition to small molecule (Proteolysis Targeting Chimera, Antibody-Drug Conjugate, etc.), antibodies(modified antibody), gene therapies (AAV technology), and cell therapies (Universal Donor Cell).

Related information;
STORY(PROTAC)
STORY(Cell therapy)

In pharmacokinetic research, we predict and understand pharmaceutical activities, and through drug delivery studies utilizing drug delivery system (DDS) technologies, we conduct logical analyses to maximize the effectiveness of clinical development candidates through accurate clinical predictions (pharmacokinetic-pharmacodynamic [PK-PD], metabolites, PK/drug-drug interactions [DDI]). In order to predict and understand pharmaceutical activities, data on the pharmacokinetics and drug responses are obtained from various perspectives to comprehensively study the pharmacokinetics of the pharmaceutical product. In addition, we provide information to optimize various modalities, provide solutions for issues, and generate hypotheses. We conduct pharmacokinetic studies necessary to start clinical studies and apply for approval of new pharmaceutical products with a consistent pharmacokinetic evaluation system from the stage of early science research to clinical development.
Furthermore, as a hub function that supports the bioanalyses for clinical development studies conducted around the world, we conduct PK-PD (biomarker) measurement and analyses and also play a role from the investigational new drug (IND) stage until approval as a new pharmaceutical product is obtained.

In order to generate high-quality development candidates, we start drug discovery safety research on modalities in the early stage of research and increase the probability of generation of clinical development candidates. In addition, we confirm the safety of multiple clinical development candidates and contribute to the quality enhancement of these clinical development candidates. By conducting research using cells, etc., we accurately predict and judge the risk of adverse reactions of novel small molecule drugs and biopharmaceuticals in humans, and predict and evaluate the safety when these are administered to humans. Data obtained from safety research will be an important consideration in selecting development candidates and determining the details of clinical studies.
In collaboration with academia and venture companies within Japan and overseas, we conduct research to elucidate toxicity mechanisms that have been difficult to clarify and predict safety in clinical settings using advanced technologies such as molecular biology and digital health. In addition, we are taking on the challenge of creating new concepts in safety research, such as building safety evaluation systems for new pharmaceutical modalities.

With the development of machine learning (artificial intelligence, AI) and dramatic improvements in computer performance, data sciences have become an essential means of drug discovery research at Astellas. Digital data have been widely used to advance research, including the reconstruction of protein structures through homology modeling, etc., in silico screening in which docking simulations with compounds are performed to predict activity, detailed analyses of a large amount of high-throughput screening (HTS) data actually performed using hit history and structural clustering, etc., as well as similarity searches and scaffold hopping from compound libraries. In addition, in silico-driven antibody engineering is also contributing to drug discovery research, including the prediction of properties based on the amino acid sequence of antibodies, the simulation of aggregation prediction, and the design and improvement of properties of antibodies using existing data. In silico modeling and simulation are also used to predict pharmacokinetics.