In drug discovery, proactive toxicological risk assessment of candidate compounds is crucial for avoiding clinical-stage failure and post-market withdrawal. However, traditional preclinical safety evaluation strategies have significant limitations: in vivo animal studies exhibit low human relevance (translatability) due to significant species differences and face dual pressures of ethics and cost; while conventional in vitro binding assays are inadequate for predicting functional biological effects. Notably, approximately 75% of clinical adverse drug reactions (ADRs) originate from dose-dependent off-target effects, highlighting an urgent need for more accurate early-risk identification tools. To address these challenges, the IQ DruSafe consortium— a preeminent alliance of global pharmaceutical companies—has championed the enhancement of preclinical predictive power through the expansion and refinement of secondary pharmacology screening strategies. This study is aligned with this initiative and aims to investigate the implementation of an advanced in vitro secondary pharmacology screening system utilizing functional assay formats. By generating richer pharmacological information beyond mere binding affinity, this strategy seeks to enable a more accurate and earlier identification of potential safety liabilities, thereby providing highly translatable safety insights for the optimization of lead compounds.
We present an integrated hit discovery platform for B7‑H3 macrocyclic peptide binder screening that synergizes high‑throughput biophysical screening (SPR, SPS) with phage display technology. State-of-the-art protein structure prediction methods were employed to model the peptide–receptor complex, combined with binding free-energy calculations, thereby enabling the rapid identification and optimization of high-affinity cyclic peptide binders against oncology targets.
Antibody-drug conjugates (ADCs) are key targeted therapies, yet drug resistance remains a major clinical challenge. To address this, we established a panel of 28 well-characterized ADC-resistant cell lines, validated via resistance profiling, RNA-seq, and WES analysis. This platform enables high-throughput screening of novel payloads and combinations to overcome resistance.
Poly (ADP-ribose) polymerase inhibitors (PARPi), such as Olaparib, have revolutionized the targeted treatment of ovarian cancer. However, the emergence of acquired drug resistance significantly limits their long-term clinical efficacy. The molecular mechanisms driving Olaparib resistance are highly heterogeneous, involving dynamic cellular state transitions and complex multi-level regulations that cannot be fully captured by bulk profiling alone. In this study, we established a comprehensive multi-omics integration framework (scRNA-seq, ATAC-seq, WES, and bulk RNA-seq) to delineate the evolutionary trajectory and chromatin regulatory landscape of Olaparib-resistant ovarian cancer cells. Furthermore, we developed an AI-driven drug screening pipeline integrating machine learning feature extraction and deep learning affinity prediction to identify potential reversal agents. This study provides a systematic multi-dimensional map of PARPi resistance and offers a reliable translational workflow for discovering synergistic combination therapies.
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