Giulia Apprato

Targeted protein degradation has emerged as a clinically validated therapeutic strategy, culminating in the recent FDA approval of the first proteolysis‐targeting chimeras (PROTAC) drug (VEPPANU). Within PROTACs, the linker plays a central role in modulating degradation efficiency as well as physicochemical and in vitro absorption, distribution, metabolism, and excretion properties, yet linker optimization still largely relies on empirical “linkerology” approaches. Here, we investigated whether simple 2D linker descriptors could provide informative insights for very early‐stage PROTAC design. Four matched PROTAC series, including both cereblon‐ and von Hippel–Lindau‐recruiting degraders, were retrospectively analyzed using a workflow combining the in‐house Bellerophon‐based linker extraction with principal component analysis. Selected descriptors captured linker hydrophobicity, polarity, flexibility, and topology and were correlated with experimental degradation, permeability, solubility, lipophilicity, protein binding, and metabolic stability data. In several datasets, linker‐dependent trends associated with the molecular properties of degraders and their degradation efficiency were identified, particularly for von Hippel–Lindau‐based PROTACs. Although the limited size of currently available datasets prevents broad generalization, this work highlights the potential of simple descriptor‐based analyses to support early candidate prioritization and reduce unnecessary synthetic efforts.

Proteolysis-targeting chimeras (PROTACs) represent a promising modality for targeted protein degradation, yet their structural complexity complicates systematic design and analysis. Bellerophon is a new computational tool that automatically decomposes PROTACs into their warhead, linker, and E3 ligase ligand directly from molecular structure. By enabling automated and standardized decomposition of degraders, the tool facilitates drug design at different levels: Bellerophon demonstrated versatility for moiety replacement (ARV-110), large-scale annotation (PROTAC-DB) and linker analysis (IRAK4 data set). The tool is freely available through a user-friendly web interface, with open-source code to encourage transparency and collaborative development in chemical biology and medicinal chemistry.

To enhance drug discovery efforts, medicinal chemists should evaluate, filter, and utilize relevant structural information about target proteins. Acquiring and interpreting protein structures is crucial for elucidating ligand‐receptor interactions and addressing ADME‐related considerations, making it an essential aspect of medicinal chemistry. Proteins exhibit diverse structural organizations, and their conformational variability poses challenges for drug design. Here we draw from our own experience how advances in structural biology have improved medicinal chemists’ access to 3D protein structures and dynamics, while stressing the need for model validation and refinement. To highlight the influence of structural biology on drug discovery, we present case studies. Structural insights into P‐glycoprotein have been crucial in addressing multidrug resistance, while the analysis of the oncoprotein β‐catenin provides valuable guidance for developing future therapeutic strategies based on innovative technology like targeted protein degradation. Finally, we report about multimeric complexes of proteins (Alsin and AP‐4) involved in rare HSPs and the contribution of protein–protein interaction analyses to discover mutation‐specific drugs.

The development of CNS-active PROTACs is limited by the blood–brain barrier (BBB). This Microperspective briefly reviews BBB physiology and critically evaluates current in silico, in vitro, and in vivo models used to assess brain penetration. These considerations enable a unified analysis of the 30 published 30 CNS-targeted degraders compiled here for the first time, highlighting the lack of a consistent design strategy and offering perspectives for future programs.

Degraders are an increasingly important sub-modality of small molecules as illustrated by an ever-expanding number of publications and clinical candidate molecules in human trials. Nevertheless, their preclinical optimization of ADME and PK/PD properties has remained challenging. Significant research efforts are being directed to elucidate underlying principles and to derive rational optimization strategies. In this review the authors summarize current best practices in terms of in vitro assays and in vivo experiments. Furthermore, the authors collate and comment on the current understanding of optimal physicochemical characteristics and their impact on absorption, distribution, metabolism and excretion properties including the current knowledge of Drug-Drug interactions. Finally, the authors describe the Pharmacokinetic prediction and Pharmacokinetic/Pharmacodynamic -concepts unique to degraders and how to best implement these in research projects. Despite many recent advances in the field, continued research will further our understanding of rational design regarding degrader optimization. Machine-learning and computational approaches will become increasingly important once larger, more robust datasets become available. Furthermore, tissue-targeting approaches (particularly regarding the Central Nervous System will be increasingly studied to elucidate efficacious drug regimens that capitalize on the catalytic mode of action. Finally, additional specialized approaches (e.g. covalent degraders, LOVdegs) can enrich the field further and offer interesting alternative approaches.

Background and Purpose. The classical drug discovery toolbox continually expands beyond traditional rule of five (Ro5)-compliant small molecules to include new chemical modalities for difficult-to-drug targets. The paper focuses on the molecular properties essential to drive oral bioavailability within the bRo5 framework. Experimental Approach. The first part outlines the concept and methodologies for characterizing bRo5 physicochemical properties, including considerations on chameleonicity; in particular, the paper sum­marizes the content of the last author’s talk presented during the IAPC-10 Meeting held in Belgrade in September 2023 (https://iapchem.org/index.php/iapc-10-home). The second part of the manuscript presents novel experimental and computational data on three proteolysis targeting chimeras (PROTACs) currently in clinical trials. Key Results. Molecular descriptors of ARV-110, ARV-471, and DT-2216 are reported and the main limitations of the applied experimental approaches are discussed. Moreover, a simple computational method shows how predicting the presence of chameleonic effects. Conclusion. A full complete physicochemical characterization of three degraders in clinical trial is reported to highlight the differences in physicochemical descriptors between PROTACs dosed orally and intravenously.

Proteolysis targeting chimeras (PROTACs or degraders) represent a novel therapeutic modality that has raised interest thanks to promising results and currently undergoing clinical testing. PROTACs induce the selective proteasomal degradation of undesired proteins by the formation of ternary complexes (TCs). Having knowledge of the 3D structure of TCs is crucial for the design of PROTAC drugs. Here, we describe DegraderTCM, a new computational method for modeling PROTAC-mediated TCs that requires low computational power and provides sound results in a short time span. We validated DegraderTCM against a selected set of experimentally determined structures and defined a method to predict the PROTAC degradation activity based on the computed TC structure. Finally, we modeled TCs of known degraders holding significance for defining the method's applicability domain. A retrospective analysis of structure-activity relationships unveiled possibilities for utilizing DegraderTCM in the initial stages of designing novel PROTAC drugs.

New chemical modalities in drug discovery include molecules belonging to the bRo5 chemical space. Because of their complex and flexible structure, bRo5 compounds often suffer from a poor solubility/permeability profile. Chameleonicity describes the capacity of a molecule to adapt to the environment through conformational changes; the design of molecular chameleons is a medicinal chemistry strategy simultaneously optimizing solubility and permeability. A default method to quantify chameleonicity in early drug discovery is still missing. Here we introduce Chamelogk, an automated, fast, and cheap chromatographic descriptor of chameleonicity. Moreover, we report measurements for 55 Ro5 and bRo5 compounds and validate our method with literature data. Then, selected case studies (macrocycles, nonmacrocyclic compounds, and PROTACs) are used to illustrate the application of Chamelogk in combination with lipophilicity (BRlogD) and polarity (Δ log k_w^IAM) descriptors. Overall, we show how Chamelogk deserves being included in property-based drug discovery strategies to design oral bioavailable bRo5 compounds.

A targeted bibliographic search exposed the deficiencies within existing PROTAC preclinical pipelines, including missing, poor-quality data and technical limitations in the experimental assays. Several recommendations are proposed to improve the efficiency of preclinical platforms for PROTACs.

Proteolysis-Targeting Chimeras (PROTACs) have recently emerged as a promising technology in the drug discovery landscape. Large interest in the degradation of the androgen receptor (AR) as a new anti-prostatic cancer strategy has resulted in several papers focusing on PROTACs against AR. This study explores the potential of a few in silico tools to extract drug design information from AR degradation data in the format often reported in the literature. After setting up a dataset of 92 PROTACs with consistent AR degradation values, we employed the Bemis–Murcko method for their classification. The resulting clusters were not informative in terms of structure–degradation relationship. Subsequently, we performed Degradation Cliff analysis and identified some key aspects conferring a positive contribution to activity, as well as some methodological limits when applying this approach to PROTACs. Linker structure degradation relationships were also investigated. Then, we built and characterized ternary complexes to validate previous results. Finally, we implemented machine learning classification models and showed that AR degradation for VHL-based but not CRBN-based PROTACs can be predicted from simple permeability-related 2D molecular descriptors.