Theranostics, the integration of molecular imaging and targeted radionuclide therapy, has emerged as a key paradigm in modern oncology. The concept dates back to the use of radioactive iodine for thyroid diseases in the 1940s, where the same molecular pathway enabled both diagnosis and treatment. In recent years, theranostic approaches have rapidly expanded with the clinical success of peptide receptor radionuclide therapy (PRRT) for neuroendocrine tumors and prostate-specific membrane antigen (PSMA)-targeted therapy for metastatic prostate cancer. These developments have transformed nuclear medicine from a primarily diagnostic discipline into a therapeutic specialty.

The fundamental principle of theranostics is the use of matched diagnostic and therapeutic radiopharmaceuticals that target the same biological pathway. Molecular imaging enables noninvasive whole-body assessment of target expression, patient selection, and treatment monitoring. Therapeutic radionuclides, including beta and alpha emitters, deliver cytotoxic radiation selectively to tumor cells while minimizing damage to surrounding tissues.

Recent advances highlight the growing importance of personalized dosimetry. Rather than administering fixed activities, quantitative imaging and absorbed-dose calculations are increasingly used to optimize treatment efficacy and safety.

In this lecture, we will review the historical development of radionuclide therapy, current clinical applications such as PRRT and PSMA-targeted therapy, and emerging alpha-emitter treatments. we will also discuss the role of quantitative imaging and dosimetry in achieving truly personalized cancer treatment, and outline future perspectives for theranostics in the era of precision oncology.

In spring, many animals become more active and prepare for reproduction. Additionally, some animals migrate in autumn, and in winter, they hibernate or enter dormancy to survive the harsh season. As these examples demonstrate, animals can appropriately sense seasonal changes and flexibly alter their physiological functions and behaviors. Many animals adapt to seasons by relying on information from day length and temperature, but it is also known that they actively adapt to seasonal changes by utilizing an internal biological clock that maintains a rhythm with an approximately one-year cycle. However, many aspects of the molecular mechanisms underlying seasonal adaptation in animals remain unclear.

Abstract:
Recent developments in biotechnology have contributed to the increase in the amounts of high-throughput data in the genome, transcriptome, proteome, interactome, phenome and diseasome. At the same time, the high-throughput screening of large-scale chemical compound libraries with various biological assays is enabling us to explore the chemical space of possible compounds. These big data can be useful resources for drug development processes. Artificial intelligence and machine learning methods are expected to play key roles in the big data analysis. In this study, we developed novel machine learning methods for various applications in drug discovery.

First, we present a computational approach for therapeutic target identification. A critical element of drug development is the identification of therapeutic targets for diseases, but the depletion of therapeutic targets is a serious problem. Here we propose to integrate genome-wide and transcriptome-wide association studies for predicting new therapeutic targets of various diseases including orphan diseases. Next, we present a network-based and structure-based approach for discovering therapeutic and side effects of drug candidate compounds. The prediction is performed based on molecular interaction networks and genome-side scale protein structures revealed by AlphaFold. Finally, we present a computational approach for de novo drug design. Most previous methods are based on chemical information, but they do not take into account biological information. Here we propose to utilize various biological data and polypharmacology toward the de novo design of drug candidate molecular structures with desired properties.

Modern biology and medicine increasingly rely on high-dimensional spatio-temporal data such as live-cell imaging and whole-brain medical images. While machine learning methods provide powerful tools for predictive analysis, they do not necessarily provide insight into the underlying biological mechanisms. In this talk, I present a unified framework that combines biophysics modeling with machine learning methods to extract hidden physical and biochemical information from image data. Such an approach has long been studied in engineering under the name of inverse problem/analysis. However, in biomedical sciences, mathematical models tend to be far more uncertain and complex, which poses challenges in modeling, simulation and learning [1,2].

First, I introduce a fortunate case in which the material properties of cultured epithelia can be inferred by using textbook machine learning methods [3]. By coupling cellular velocity and force measurements with a growing elastic sheet model, we estimate stresses and material parameters without applying invasive external forces, revealing an unusual mechanical behavior consistent with epithelial function.

Second, I turn to a more challenging case in which effective biochemical heterogeneity governing the progression of Alzheimer's disease is inferred from human brain PET scans. A core difficulty is that we need to estimate millions of parameter values to fully characterize biochemical heterogeneity across the whole brain. To address this issue, we formulate a reaction-diffusion model on realistic brain geometry and implement it in a differentiable simulation framework, which enables efficient learning via error backpropagation through time. We then estimate the spatially heterogeneous amplification rate of pathological tau, a quantity that is otherwise inaccessible to direct measurement.

Finally, I will discuss open problems and future directions, with a particular focus on applications to spatial omics data.