TY - JOUR
T1 - Modeling combination therapies in patient cohorts and cell cultures using correlated drug action
AU - Arun, Adith S.
AU - Kim, Sung Cheol
AU - Ahsen, Mehmet Eren
AU - Stolovitzky, Gustavo
N1 - We thank the Columbia University High Throughput Screening Facility and the Sulzberger Columbia Genome Center, where the viability experiments were conducted by Charles Karan and Ronald B. Realubit. We also thank Andrea Califano for providing the LINCS screen of 990 drug combinations from where we chose the drug combinations for cell viability used in this paper. G.S. conceived of the project. All co-authors developed the methodology for the project. A.S.A. and S.C.K. developed computational approaches and performed bioinformatics analysis with guidance from G.S. and M.E.A. A.S.A. drafted the manuscript and all authors participated in its editing and revision. The authors declare that they have no competing interests.
PY - 2024/3/15
Y1 - 2024/3/15
N2 - Characterizing the effect of combination therapies is vital for treating diseases like cancer. We introduce correlated drug action (CDA), a baseline model for the study of drug combinations in both cell cultures and patient populations, which assumes that the efficacy of drugs in a combination may be correlated. We apply temporal CDA (tCDA) to clinical trial data, and demonstrate the utility of this approach in identifying possible synergistic combinations and others that can be explained in terms of monotherapies. Using MCF7 cell line data, we assess combinations with dose CDA (dCDA), a model that generalizes other proposed models (e.g., Bliss response-additivity, the dose equivalence principle), and introduce Excess over CDA (EOCDA), a new metric for identifying possible synergistic combinations in cell culture.
AB - Characterizing the effect of combination therapies is vital for treating diseases like cancer. We introduce correlated drug action (CDA), a baseline model for the study of drug combinations in both cell cultures and patient populations, which assumes that the efficacy of drugs in a combination may be correlated. We apply temporal CDA (tCDA) to clinical trial data, and demonstrate the utility of this approach in identifying possible synergistic combinations and others that can be explained in terms of monotherapies. Using MCF7 cell line data, we assess combinations with dose CDA (dCDA), a model that generalizes other proposed models (e.g., Bliss response-additivity, the dose equivalence principle), and introduce Excess over CDA (EOCDA), a new metric for identifying possible synergistic combinations in cell culture.
KW - Applied computing
KW - Computational chemistry
UR - http://www.scopus.com/inward/record.url?scp=85187258938&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85187258938&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2024.108905
DO - 10.1016/j.isci.2024.108905
M3 - Article
C2 - 38390492
AN - SCOPUS:85187258938
SN - 2589-0042
VL - 27
JO - iScience
JF - iScience
IS - 3
M1 - 108905
ER -