Integrating In Vitro Analytics for Improved Antibody–Drug Conjugate Candidate Selection.
Simple Summary: 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 s...
| Publicado en: | Cancers Vol. 18; no. 1; pp. 164 - 179 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190788340&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190788340 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jan2026 vid: 18 iid: 1 pid: 97109 pub: MDPI artinfo: ui: 190788340 190788340 190788340 10.3390/cancers18010164 190788340 ppf: 164 ppct: 15 formats: tig: atl: Integrating In Vitro Analytics for Improved Antibody–Drug Conjugate Candidate Selection. aug: au: del Solar, Virginia Saleh, Ali Di Tacchio, Annarita Becciolini, Lena Sokol Kang, Gyoung Dong Jackowska, Bianka Hu, Yan Gong, Chao Zhang, Angel Hostetler, Leigh Lee, Maximilliam Khan, Akbar H. Mitra, Abhisek Ahmed, Mahammad Tickle, David Vijayakrishnan, Balakumar affil: Oncology Targeted Discovery-Drug CandidateDC, Chemistry, AstraZeneca, London E1 2AX, UK sug: subj: Antibodies Pharmacokinetics Drug Development Chemistry, Analytical Animal Studies In Vitro Studies Data Analysis Software In Vivo Studies Biological Products Descriptive Statistics Drug Stability Bioinformatics Prediction Models Models, Biological Mice Two-Tailed Test Linear Regression ab: Simple Summary: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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