Advancing clinical outcome predictions via incorporating pharmacokinetic simulations into in vitro testing - a colorectal cancer example
Poloznikov, A.; Britt, B. R.; Nikulin, S.; Rodin, S.; Grinnemo, K.-H.; Woywod, M.; Farouq, J.
Show abstract
The development of in vitro assays that can predict clinical outcomes is highly desirable for drug development and personalized medicine. However, conventional in vitro methods often fail to replicate physiological drug pharmacokinetics, posing a challenge to their clinical translation. To address this issue, we adjusted incubation times and concentrations of standard-of-care drugs in the in vitro chemosensitivity assay to reflect those encountered by colorectal cancer patients. Then, for first time, we mimicked the relevant drug exposure of mFOLFOX-6, CapOx and FOLFIRI protocols to predict clinical outcomes. Our pharmacokinetic-based testing on primary colorectal cancer cells accurately predicted responders and non-responders among a cohort of patients (N=6). Classical testing methods such as IC50 and GI50 did not reveal any clinically meaningful results. Furthermore, we demonstrated that even subtle changes in drug incubation times could lead to significant variations in the classification of cells as sensitive and resistant, which is not related to mechanisms of action according to categorical clustering. Finally, our pharmacokinetic-based test results were consistent with the historical clinical data on similarities of mFOLFOX-6 and CapOx schemes. Our results contribute to the growing body of evidence that pharmacokinetic-based in vitro testing could bridge the gap between laboratory research and clinical practice. Integration of pharmacokinetic dynamics into in vitro tests could have a significant potential in enhancing drug development and refining personalized treatment strategies.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- PBPK modelling of dexamethasone in patients with COVID-19 and liver disease 92%
- CETP inhibitor evacetrapib enters mouse brain tissue 91%
- Development of a Novel Bruton's Tyrosine Kinase Inhibitor that exerts Anti-Cancer Activities Potentiates Response of Chemotherapeutic Agents In Multiple Myeloma Stem Cell-Like Cells 90%
Similar papers in this journal
- Reshaping the Landscape of Locoregional Treatments for Breast Cancer Liver Metastases: A novel, intratumoral, p21-targeted percutaneous therapy increases survival in BALB/c mice inoculated with 4T1 triple negative breast cancer cells in the liver. 93%
- Comprehensive Live-cell Imaging Analysis of Cryptotanshinone and Synergistic Drug-Screening Effects in Various Human and Canine Cancer Cell Lines 93%
- A Priori Activation of Apoptosis Pathways of Tumor (AAAPT) Technology: Development of Targeted Apoptosis Initiators for Cancer Treatment. 93%
Similar papers in this journal
- A model of Zebrafish Avatar for co-clinical trials 94%
- Synthetic lethality screening identifies FDA-approved drugs that overcome ATP7B-mediated tolerance of tumor cells to cisplatin 94%
- Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma 94%
Similar papers in this journal
- Improved bioavailability of montelukast through a novel oral mucoadhesive film in humans and mice 91%
- Mesoscopic Fluorescence Imaging of Light-Triggered Chemotherapeutic Release in Cancer Spheroid Models 91%
- Interactions of anti-COVID-19 drug candidates with multispecific ABC and OATP drug transporters 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.