# Integrating In Vitro Analytics for Improved Antibody–Drug Conjugate Candidate Selection

## TL;DR

This paper introduces a new method combining lab experiments and data analysis to better select effective antibody-drug conjugates for cancer treatment.

## Contribution

The novel contribution is an integrated workflow combining in vitro analytics and data analysis to improve ADC candidate selection.

## Key findings

- In vitro stability profiles correlate with in vivo pharmacokinetic data.
- Software-driven analysis accelerates decision-making in ADC triage.
- Integrated methods identify ADCs with optimal pharmacodynamic characteristics.

## Abstract

Antibody–drug conjugates (ADCs) are targeted antitumoral medicines that bind potent drugs to antibodies, directing treatment to cancer cells and reducing side effects. In early drug development, finding the optimal ADC candidates was scientifically and technically challenging since several critical factors must be considered, such as ensuring these drugs remain stable in the plasma during circulation or confirming they can release their payload where it is needed. Herein, we combine laboratory-based analytical workflows with advanced data analysis pipelines to quickly assess the stability and efficacy of ADC candidates. Our approach helps in selecting the promising candidates for further development by identifying the transformations an ADC undergoes in serum and the efficacy on releasing their payloads. This integrated method allows us to analyse a larger set of compounds efficiently and supports the discovery of safer, more effective cancer treatments.

Background/Objectives: The development of antibody–drug conjugates (ADCs) presents significant scientific and operational challenges, from optimising conjugation chemistry and linker stability to establishing robust analytical controls. Advanced analytical methods, particularly the combination of plasma stability assays with enzymatic studies, are essential for early screening and characterisation of ADC candidates. Integrating these in vitro assays with powerful data analysis software accelerates structure–activity relationship assessments and the identification of stable compounds in plasma. Methods: This article examines how combined analytical and computational approaches enhance candidate selection by offering valuable insights into the metabolic fate and stability risks of ADCs. Results: Our research shows correlation between in vitro stability profiles and in vivo pharmacokinetic (PK) data, demonstrating the predictive power of early-stage analytical studies. Implementation of software-driven visualisation and analysis enables faster, data-informed decision making, streamlining the triage process to prioritise candidates with optimal PK and pharmacodynamics (PD) characteristics. Conclusions: These findings highlight the critical need for integrated in vitro analytics and computational tools in efficient ADC development, supporting the selection of candidates with the greatest potential for clinical success and facilitating a more effective and accelerated path from discovery to clinical application.