Target-based drug discovery relies on the identification of disease-modifying targets and the validation of their modulation as therapeutic opportunity. Various approaches can be used to act on specific pathways and some of the most described modalities use specific molecules to bind validated targets and interfere with molecular mechanisms involved in the pathogenesis and progression of a disease.

Biophysical methods are widely used in drug discovery, as primary assays for the identification of novel hits or as orthogonal assays to profile selected molecules. In addition to the information obtained from classical enzymatic assays, biophysical methods can demonstrate direct binding to the target of interest and can inform on binding stoichiometry, affinity and kinetic parameters.

The characterization of molecular interactions represents a fundamental step in the screening cascade, to elucidate key information required for the progression of novel molecules. A variety of assays with different levels of complexity can be used, and the choice of the most suitable technology is guided by the nature of the target, the stage of the project and the key information required.

The seminar will present an overview of some biophysical techniques that can be applied for the characterization of binding interactions, with a focus on their applicability for drug discovery and on the key aspects that are considered to choose, design and validate new assays.

Achieving sufficient target engagement is a central objective in drug discovery and requires the optimization of multiple physicochemical and pharmacokinetic properties beyond potency alone. The role of ADME (Absorption, Distribution, Metabolism, and Excretion) assays in guiding medicinal chemistry decisions will be addressed, with particular emphasis on the interpretation of experimental data and their limitations, and on how in vitro findings can be translated into confidence in target-site exposure.

A critical overview of key in vitro ADME assays routinely employed during hit-to-lead and lead optimization phases will be provided, including solubility, permeability, metabolic stability, and protein binding. For each assay, methodological aspects, throughput and, most importantly, strengths and limitations will be discussed from a medicinal chemist s perspective, highlighting potential pitfalls in data interpretation.

The concept of free drug concentration as the main driver of pharmacological activity will be examined, with particular attention to how plasma and tissue binding influence the interpretation of potency and exposure2. The interplay between permeability, active transport, and tissue distribution will also be considered as key determinants of compound access to the site of action.

Furthermore, the integration of ADME parameters to support the prediction of in vivo pharmacokinetics and the estimation of target engagement will be discussed, including selected case examples.

Overall, this lecture aims to provide medicinal chemists with a practical framework for the critical interpretation of ADME data in the context of target engagement, ultimately enabling a more informed and rational design of compounds with an increased probability of in vivo success.

References
[1] Wernevik J. et al., Assay Drug Dev Technol 2020,18(4):157
[2] Smith D. J. et al., Nat Rev Drug Discov 2010, 9(12):929-39.

The Cellular Thermal Shift Assay (CETSA) is a biophysical method that enables direct measurement of drug-induced protein interaction states inside living cells and tissues. The assay exploits a fundamental principle of protein biochemistry: binding of a ligand to its target protein can affect the protein's thermal stability. By exposing cells or lysates to a range of temperatures, allowing heat-induced denaturation and precipitation of proteins, and analyzing the remaining soluble fraction, CETSA provides a direct readout of protein states in a physiologically relevant context, without the need for protein purification or chemical labelling [1].

The presentation will follow the evolution of CETSA from its origins as a target engagement tool to its most advanced proteome-wide implementations [2]. Starting from the basic melt curve experiment in cell lysates, we will progress through whole-cell and tissue applications, isothermal dose-response formats, and time-dependent measurements. We will then cover the coupling of CETSA with quantitative mass spectrometry, enabling simultaneous profiling of thousands of proteins, and conclude with IMPRINTS-CETSA, a systems-level approach now being used to dissect drug mechanisms of action [4], identify resistance mechanisms [5], discover mechanistic biomarkers, and map global changes in protein interaction states that underlie fundamental cellular processes [3].

References
[1] Jafari R, Almqvist H, Axelsson H, et al. Nat Protoc. 2014 Sep;9(9):2100-22.
[2] Dai L, Prabhu N, Yu LY, Bacanu S, Ramos AD, Nordlund P. Annu Rev Biochem. 2019.
[3] Liang YY, Bacanu S, Ramos AD, et al. Cell Chem Biol. 2022 Apr 21;29(4):572-585.
[4] Liang YY, Khalid K, Le HV, et al. Nat Commun. 2025 May 7;16(1):4234.
[5] Ramos AD, Liang YY, Surova O, et al. Cell Rep. 2024 Oct 22;43(10):114784.

Understanding cellular metabolism in real time is essential for deciphering biological responses to environmental changes, drug treatments, and disease-related perturbations. This lecture provides an educational introduction to the use of magnetic resonance spectroscopy (MRS) as a non-invasive tool for monitoring cellular metabolism, with a specific focus on the InsightMR accessory.

The first part of the lecture will introduce the fundamental principles of magnetic resonance spectroscopy, highlighting how metabolic information can be extracted from MR signals. Key concepts such as spectral resolution, metabolite identification, and quantitative analysis will be discussed with illustrative examples.

The second part will focus on real-time metabolic monitoring in living cells, emphasizing the experimental challenges associated with dynamic biological systems. InsightMR will be presented as an innovative solution designed to integrate controlled cell culture conditions with MR measurements, enabling continuous and time-resolved observation of metabolic processes.

Finally, selected applications will be discussed to demonstrate how InsightMR-based MRS can be used to investigate cellular energetics, metabolic fluxes, and responses to pharmacological stimuli. By the end of the lecture, students will gain a clear understanding of both the theoretical basis and the practical potential of real-time MR spectroscopy for studying cellular metabolism.

References
[1] Damian Hertig, Sally Maddah, Roman Memedovski, Sandra Kurth, Aitor Moreno, Matteo Pennestri, Andrea Felser, Jean-Marc Nuoffer and Peter Vermathen Analyst, 2021, 146, 4326.
[2] Enrico Luchinat, Letizia Barbieri, Timothy F. Campbell, and Lucia Banci Anal. Chem. 2020, 92, 9997-10006

Virtually every pharmaceutical agent is, in essence, a molecule capable of selective binding to a protein target of interest. Accordingly, the discovery of pharmaceutically relevant targets and of binding molecules (hits) represents a central challenge in pharmaceutical science. Among modern pharmaceutical products, targeted therapeutics, including antibody-drug conjugates and radioligand therapeutics, are emerging as powerful tools to treat disease, particularly cancer. [1] In this lecture, we will explore how mass spectrometry can serve as a key enabling technology in the development of such therapeutics. In particular, we will discuss applications for the identification of (i) vasculature-accessible target proteins and (ii) binding molecules for pharmaceutically relevant targets.

References
[1] Cazzamalli S., Puca E., Neri D., Nat Cancer. 2025 (9):1494-1504

In drug discovery, the prediction of sites of metabolism (SoM) in xenobiotics is a key challenge, as metabolic transformations strongly affect pharmacokinetics, efficacy, and safety. Early computational approaches were mainly based on structure- and knowledge-driven models, providing mechanistic insight into cytochrome P450-mediated metabolism. Among these, MetaSite represented a milestone by combining chemical reactivity with enzyme accessibility to rationalize SoM [1, 2]. Over time, MetaSite has evolved beyond CYP-mediated metabolism to include additional Phase I and Phase II enzymes [3, 4], and selectivity prediction has also been introduced for specific enzyme systems.

More recently, data-driven methodologies, including machine learning and deep learning approaches, have been increasingly applied to SoM prediction, primarily focusing on cytochrome-mediated metabolism [5, 6].

A particularly successful strategy has been the integration of in silico metabolism prediction with experimental LC-MS/MS data, enabled by recent advances in mass spectrometry. This synergy has led to the development of platforms such as MassMetaSite, allowing more efficient metabolite identification and structural elucidation [7,8].

This talk will provide an overview of these different approaches, highlighting their strengths and limitations, and discussing how their integration can improve the reliability of metabolism prediction.

References
[1] Cruciani G., Carosati E., De Boeck B., et al. J. Med. Chem. 2005, 48: 6970-6979.
[2] Cruciani G., Baroni M., Benedetti P., Goracci L., Fortuna C.G.,Drug Discovery Today: Technologies, 2013, 10: e155-e165.
[3] Cruciani G., Valeri A., Goracci L., Pellegrino R.M., Buonerba F., Baroni M. J. Med. Chem. 2014 57:6183-96.
[4] Cruciani G., Milani N., Benedetti P., Lepri S., Cesarini L., Baroni M., Spyrakis F., Tortorella S., Mosconi E., Goracci L. J. Med. Chem. 2018 6:360-371.
[5] Sicho M., Stork C., Mazzolari A., de Bruyn Kops C., Pedretti A., Testa B., Vistoli G., Svozil D., Kirchmair J. J. Chem. Inf. Mod, 2019, 59: 3400-3412.
[6] Litsa E.E., Das P., Kavraki L.E. Expert. Opin. Drug Metab. Toxicol. 2021, 17:1245-1247.
[7] Bonn B., Leandersson C., Fontaine F., Zamora I. Rapid Commun. Mass. Spectrom. 2010, 24:3127-38.
[8] Goracci L., Desantis J., Valeri A., Castellani B., Eleuteri M., Cruciani G. J. Med. Chem., 2020 63:11615-11638.

Early-stage drug development requires rapid and reliable methods to identify and characterize interactions between small-molecule candidates and their biological targets. Native mass spectrometry (MS) has emerged as a powerful, complementary analytical approach due to its ability to preserve noncovalent interactions during analysis.[1] This enables the direct observation of intact protein-ligand complexes and the simultaneous detection of multiple coexisting species, allowing for the determination of binding stoichiometry, assessment of ligand specificity, and estimation of relative binding affinities. The technique is particularly well-suited for complex biological systems, including those exhibiting structural heterogeneity or transient interactions.

In this talk, I will present how native MS can be applied across the early drug development workflow. Specific examples include the determination of binding stoichiometry and ligand binding, as well as the use of native MS to troubleshoot and orthogonally validate results for DNA-encoded libraries.[2] In addition, the integration of ion mobility spectrometry enables the assessment of ligand-induced changes in protein conformation and overall structural organization. Finally, I will discuss how hydrogen deuterium exchange can be used to investigate the binding site of ligand binding. Together, these approaches highlight the versatility of MS as a rapid, informative, and increasingly accessible tool for supporting early-stage drug discovery.

References
[1] Leney, A.C. and A.J. Heck, J. Am. Soc. Mass Spectrom., 2017. 28(1): p. 5-13.
[2] Bittner, P., et al., Analytical Chemistry, 2025. 97(37): p. 20412-20421.

Nuclear Magnetic Resonance (NMR) spectroscopy represents a powerful and versatile tool for investigating ligand-protein interactions at atomic resolution, providing both structural and dynamic information. This contribution will provide an overview of the main NMR approaches used to characterize molecular recognition processes, with a focus on both protein-based and ligand-based techniques. Protein-observed methods, such as chemical shift perturbation and relaxation-based experiments, enable the mapping of binding sites and the evaluation of interaction dynamics. Complementarily, ligand-based techniques, including STD-NMR, DOSY, and relaxation-edited experiments, allow efficient screening and characterization of weak binders.

These methodologies are illustrated through their application to the RNA-binding protein HuR (ELAVL1), a key regulator of mRNA stability involved in cancer and inflammation. Drawing on recent studies, this presentation will demonstrate how integrated NMR strategies can provide detailed insights into ligand binding modes, affinity, and specificity, supporting the rational design of modulators targeting protein-RNA interactions.

References
[1] Meyer B. and Peters T. Angewandte Chemie 2003, 42 (8): 864-890.
[2] Gado I., Garbagnoli M., Ambrosio F.A., Listro R., Parafioriti M., Cauteruccio S., Rossi D., Linciano P., Costa G., Alcaro S., Vasile F., Collina S. ACS Omega. 2024, 9: 45147-45158.

The identification and characterization of biologically active compounds in early drug discovery relies heavily on robust and scalable activity assays. Within high-throughput screening and profiling campaigns, enzymatic and functional assays represent complementary approaches to assess compound activity, each providing distinct types of information and presenting specific advantages and limitations.¹

Enzymatic assays are widely used due to their robustness, relative simplicity, and compatibility with miniaturized high-throughput formats. They enable direct measurement of target modulation and are well suited for the rapid screening of large compound libraries. However, their reduced biological complexity may limit their ability to fully capture compound behavior in more physiologically relevant environments.^1,2

In contrast, functional and cell-based assays provide a more integrated view of compound activity by reflecting pathway modulation and cellular responses. These assays can improve the translational relevance of early findings but often come with increased variability, lower throughput, and additional challenges in assay development and data interpretation.^1,2

The effective use of these assay formats relies on their integration within high-throughput activity profiling workflows, where different readouts are combined in structured screening cascades to progressively refine compound characterization. In this context, assay quality, data robustness, and the identification of technical and biological artefacts play a critical role in ensuring the reliability of generated data.

Drawing on applications from drug discovery programs, the combination of enzymatic and functional readouts is discussed as a key element to strengthen confidence in activity profiles and support more informed decision-making in the early stages of the drug discovery process.

References
[1] Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011 Mar;162(6):1239-49. doi: 10.1111/j.1476-5381.2010.01127.x. PMID: 21091654; PMCID: PMC3058157.
[2] Blay V, Tolani B, Ho SP, Arkin MR. High-Throughput Screening: today's biochemical and cell-based approaches. Drug Discov Today. 2020 Oct;25(10):1807-1821. doi: 10.1016/j.drudis.2020.07.024. Epub 2020 Aug 12. PMID: 32801051.

Artificial intelligence is reshaping early drug discovery by enabling data-driven decisions across the pipeline, from target identification to preliminary developability assessment. Rather than replacing experimental science, modern AI complements medicinal chemistry and pharmacology by helping prioritize hypotheses and extract meaningful patterns from complex chemical and biological data.

This presentation provides an overview of applied AI strategies in early drug discovery, including ligand- and structure-based modeling, virtual screening, and generative design. Particular attention will be given to how these computational workflows can support the interpretation of experimental data, improve compound prioritization, and help identify safety liabilities earlier in the discovery process.

The lecture will also address key challenges that remain central to real-world adoption, including data quality, model interpretability, and the need for close integration between AI predictions and experimental validation. Overall, AI will be presented as an enabling framework for faster and more efficient early discovery, while offering a balanced perspective on its current capabilities and near-term limitations.

References [1] Lavecchia A. (Ed.) Applied Artificial Intelligence for Drug Discovery: From Data-Driven Insights to Therapeutic Innovation. Springer, 2026.
[2] Delre P., Lavecchia A. Drug Discovery Today 2025, 30: 104488.
[3] Delre P., Lavecchia A. J. Chem. Inf. Model. 2025, acs.jcim.5c01606.

The translation of cell and gene therapies from concept to patient care requires a convergence of scientific innovation, manufacturing infrastructure, ethical foresight, and policy reform. This lecture aims to provide a roadmap for navigating this path-advocating not only for scientific rigor but also for patient-centered, responsible advancement. As the field continues to evolve, such these approaches will be essential in shaping a future where advanced therapies are safe, accessible, and transformative across a wide spectrum of diseases.

References
1) Omarini C, Catani V, Mastrolia I, Toss A, Banchelli F, Isca C, Medici D, Ponzoni O, Brucale M, Valle F, Baschieri MC, D'Amico R, Masciale V, Chiavelli C, Caggia F, Bortolotti CA, Piacentini F, Dominici M. Extracellular vesicles-derived miR-21 as a biomarker for early diagnosis and tumor activity in breast cancer subtypes. Biomark Res. 2025 Jan 23;13(1):14. doi: 10.1186/s40364-025-00724-y.
2) Grisendi G, Dall'Ora M, Casari G, Spattini G, Farshchian M, Melandri A, Masicale V, Lepore F, Banchelli F, Costantini RC, D'Esposito A, Chiavelli C, Spano C, Spallanzani A, Petrachi T, Veronesi E, Ferracin M, Roncarati R, Vinet J, Magistri P, Catellani B, Candini O, Marra C, Eccher A, Bonetti LR, Horwtiz EM, Di Benedetto F, Dominici M. Combining gemcitabine and MSC delivering soluble TRAIL to target pancreatic adenocarcinoma and its stroma. Cell Reports Medicine 2024 Aug 20;5(8):101685.
3) Chiavelli C, Prapa M, Rovesti G, Silingardi M, Neri G, Pugliese G, Trudu L, Dall'Ora M, Golinelli G, Grisendi G, Vinet J, Bestagno M, Spano C, Papapietro RV, Depenni R, Di Emidio K, Pasetto A, Nascimento Silva D, Feletti A, Berlucchi S, Iaccarino C, Pavesi G, Dominici M. Autologous anti-GD2 CAR T cells efficiently target primary human glioblastoma. Nature PJ Precis Oncol. 2024 Feb 1;8(1):26.
4) Golinelli G, Grisendi G, Dall'Ora M, Casari G, Spano C, Talami R, Banchelli F, Prapa M, Chiavelli C, Rossignoli F, Candini O, D'Amico R, Nasi M, Cossarizza A, Casarini L, Dominici M. Anti-GD2 CAR MSCs against metastatic Ewing's sarcoma. Transl Oncol. 2022 Jan;15(1):101240. doi: 10.1016/j.tranon.2021.101240.

Since the creation and definition of the regulated quality standards (GxP), there has been the question about the management of research data and their importance in the frame of submissions of molecules to Regulatory Agencies.

Merck KGaA was one of the forerunners of the implementation of a dedicated voluntary quality standard defining high principles to be followed during early-research activities.

Initially introduced in 2011 with the name of QMS (Quality Management System), this new voluntary quality standard became the backbone of credible, ethical and impactful early-research management. At its core, the newly named Good Research Practice (GRP) quality standard is about integrity, quality and responsibility at every stage of the research process. Good Research Practice (GRP) evolved over the years, shaped by scientific progress, regulatory reforms, and the global push for standardization. Research environment became more systematic, somehow regulated, and ethically governed.

The key GRP principles are based on:
- Research Integrity and Accountability.
- Methodological Rigor to confirm reproducibility of the experiments.
- Ethical Conduct assuring Data Protection.
- Responsible Data Management to support Open Science and sharing of data.
- Collaboration and Authorship to maintain clear roles and responsibilities and respectful communication.
- Publication and Dissemination to disclose conflicts of interest and assess both positive and negative results.
- Continuous Learning to stay updated and seek feedback.

At Merck KGaA, Good Research Practice is critical for maintaining scientific excellence, ensuring compliance with regulatory standards, and fostering trust in research outcomes.

The presentation will focus on the history of Good Research Practice, its application to early- discovery activities, the recent OECD guidance on the Generation, Reporting and Use of Research Data for Regulatory Assessments which bridge the gap between the increasing amount of non-standard research data and the need for robust scientific evidence to inform regulatory assessments.

References
ENV/CBC/MONO(2025)18, OECD Guidance Document on the Generation, Reporting and Use of Research Data for Regulatory Assessments, 31OCT2025

Evotec delivers accelerated and highly robust characterization of complex biomolecules through its expert analytical platforms. This capability spans oligonucleotides, monoclonal antibodies (mAbs) to non-canonical biologics formats (Fab, scFv, nanobodies, ...) antibodies, and antibody-drug conjugates (ADCs). Evotec offers fully integrated project support, where this analytical platform plays a critical role in ensuring robust quality control of the molecule.

Oligonucleotides are analyzed using Ion Pair Reverse Phase (IPRP) and Size Exclusion Chromatography (SEC) coupled to high resolution mass spectrometry enable efficient separation of species, index purity UV and accurate mass determination. These approaches support the monitoring of single strand and duplex siRNA species, confirming individual strand masses and duplex formation.

Antibodies and ADCs are characterized using complementary top down and bottom-up MS approaches, allowing precise determination of the Drug to Antibody Ratio (DAR), Drug Load Distribution (DLD), free drug detection, detailed glycosylation pattern, and localization of conjugated payloads. The combined use of native (SEC MS) and denaturing conditions (RP MS) on conjugates offers a comprehensive view of both covalent and non covalent complexes, particularly for Antibody Oligonucleotide Conjugates (AOCs).

In addition to large-molecule analytics, small molecules are routinely assessed using a range of chromatographic and ionization techniques. These approaches enable index purity UV, mass measurement, and impurity identification. High-resolution Mass Spectrometry (MS) combined with targeted fragmentation (MS/MS) supports confident structural elucidation and identification of impurities, ensuring comprehensive characterization throughout the development process.

To conclude, these automated and integrated analytical approaches power a robust framework that accelerates biotherapeutic development and ensures high quality characterization throughout drug discovery.

Chemometrics and machine learning are increasingly central to modern analytical workflows, yet their adoption is often hindered by the lack of accessible, user-friendly tools. This lecture introduces two open-source software platforms: (i) CACTUS (Chemometrics and Analytical Chemistry Tools) and (ii) MVA (Methods Validation App). Both the software are designed to lower the barrier to multivariate data analysis and analytical method validation in pharmaceutical and chemical sciences.

CACTUS provides an integrated environment for exploratory data analysis, classification, regression, and variable selection, built in R and Python with an intuitive graphical interface. MVA focuses on the statistical validation of analytical methods, implementing standard protocols for linearity, precision, accuracy, and uncertainty estimation in compliance with regulatory guidelines.

The presentation will combine a conceptual overview of the underlying chemometric and ML approaches with a hands-on demonstration. Participants will be guided through practical examples using their own laptops (Windows preferred), gaining first-hand experience with both tools on representative pharmaceutical datasets.