Blood Res (2024) 59:37
Published online November 6, 2024
https://doi.org/10.1007/s44313-024-00032-8
© The Korean Society of Hematology
Correspondence to : Jamin Koo
jaminkoo@alumni.stanford.edu
†Haeryung Lee, Nahee Ko and Sujin Namgoong contributed equally to this work.
Blood cancers, including leukemia, multiple myeloma, and lymphoma, pose significant challenges owing to their heterogeneous nature and the limitations of traditional treatments. Precision medicine has emerged as a transformative approach that offers tailored therapeutic strategies based on individual patient profiles. Ex vivo drug sensitivity analysis is central to this advancement, which enables testing of patient-derived cancer cells against a panel of therapeutic agents to predict clinical responses. This review provides a comprehensive overview of the latest advancements in ex vivo drug sensitivity analyses and their application in blood cancers. We discuss the development of more comprehensive drug response metrics and the evaluation of drug combinations to identify synergistic interactions. Additionally, we present evaluation of the advanced therapeutics such as antibody–drug conjugates using ex vivo assays. This review describes the critical role of ex vivo drug sensitivity analyses in advancing precision medicine by examining technological innovations and clinical applications. Ultimately, these innovations are paving the way for more effective and individualized treatments, improving patient outcomes, and establishing new standards for the management of blood cancers.
Keywords Drug sensitivity, Blood cancers, Precision medicine, Machine learning, Prognosis
Blood Res 2024; 59():
Published online November 6, 2024 https://doi.org/10.1007/s44313-024-00032-8
Copyright © The Korean Society of Hematology.
Haeryung Lee1†, Nahee Ko1†, Sujin Namgoong1†, Seunghyok Ham2 and Jamin Koo1,2*
1 Department of Chemical Engineering, Hongik University, Seoul 04066, Republic of Korea
2 ImpriMedKorea, Inc., Seoul 03920, Republic of Korea
Correspondence to:Jamin Koo
jaminkoo@alumni.stanford.edu
†Haeryung Lee, Nahee Ko and Sujin Namgoong contributed equally to this work.
Blood cancers, including leukemia, multiple myeloma, and lymphoma, pose significant challenges owing to their heterogeneous nature and the limitations of traditional treatments. Precision medicine has emerged as a transformative approach that offers tailored therapeutic strategies based on individual patient profiles. Ex vivo drug sensitivity analysis is central to this advancement, which enables testing of patient-derived cancer cells against a panel of therapeutic agents to predict clinical responses. This review provides a comprehensive overview of the latest advancements in ex vivo drug sensitivity analyses and their application in blood cancers. We discuss the development of more comprehensive drug response metrics and the evaluation of drug combinations to identify synergistic interactions. Additionally, we present evaluation of the advanced therapeutics such as antibody–drug conjugates using ex vivo assays. This review describes the critical role of ex vivo drug sensitivity analyses in advancing precision medicine by examining technological innovations and clinical applications. Ultimately, these innovations are paving the way for more effective and individualized treatments, improving patient outcomes, and establishing new standards for the management of blood cancers.
Keywords: Drug sensitivity, Blood cancers, Precision medicine, Machine learning, Prognosis
Table 1 . Metrics used to describe drug sensitivity analysis results.
Metric | Description | Applied disease(s) and reference(s) |
---|---|---|
IC50 | The concentration of the drug by which growth or viability is inhibited or reduced by half: | ALL [5], MM [27], NHL [26] |
Emax | The (average) remaining viability of cells at maximal concentration(s): | AML [28, 29], NHL [30] |
AUC | The area under the dose–response curve, usually obtained at a fixed time point like in below: | AML [29, 31], MM [32], NHL [33] |
DSS | The integral of response over the dose range that exceeds a given minimum activity level (Cmin): | AML [14], ALL [34], MM [6] |
dDSS | The difference between DSS quantified in patient cells (patient DSS) and the average drug response of control samples: | AML [14], ALL [34] |
a Parameters: y, viability of cells (%); yUL, upper limit of y; yLL, lower limit of y; C, concentration of the drug (nM orμM); m, slope of the dose–response curve at IC50; Ci, the lowest concentration of interest; Cf, the highest concentration of interest; z, viability of controls (%).
b Abbreviations: ALL Acute lymphoblastic leukemia, MM Multiple myeloma, NHL Non-Hodgkin lymphoma.
Table 2 . The methods reported in the literature for analyzing drug combinations via sensitivity assays.
Name and reference | Description | Applied disease(s) and reference(s) |
---|---|---|
Synergy finder plus [41] | Software for analyzing drug combination synergy and sensitivity using models such as HSA, BLISS, LOEWE, and ZIP | Various cancer types [41], COVID-19 [42] |
Cross-design for drug combination sensitivity score and synergy analysis [43] | Evaluation of drug combination sensitivity and synergy by fixing one drug at a constant concentration and varying the doses of another | Various cancer types [43], AML [44] |
Multi-task learning-based synergy prediction [45] | A multi-task learning and deep neural networksbased method to predict drug combination synergy and monotherapy sensitivity | Various cancer types [45] |
Quadratic phenotypic optimization platform (QPOP) [46] | Optimizing drug combinations by analyzing drug responses using an orthogonal array composite design to efficiently test multiple drugs across various concentrations | NHL [46], various cancer types [47–49] |
Table 3 . Antibodies, antigens, and ex vivo drug sensitivity assays used to test the utility and applicability of the antibodies against the targeted diseases.
Antibody(s) | Antigen(s) | Method used to analyze drug sensitivity | Applied disease(s) and reference(s) |
---|---|---|---|
Gemtuzumab | CD33 | Colony formation (ex vivo), cytotoxicity (in vitro) | AML [49] |
AMG 330 | CD33, CD3 | Cytotoxicity (ex vivo, in vitro) | AML [50] |
IMGN632 | CD123 | Colony formation (ex vivo), cytotoxicity (in vitro), internalization/processing (in vitro) | AML [51] |
JNJ-67571244 | CD33, CD3 | Cytotoxicity (ex vivo, in vitro) | AML [52] |
NKp46-CD16a-NK Cell Engager | CD123 | Cytotoxicity (ex vivo, in vitro), NK Cell activation (in vitro) | AML [53] |
Obinutuzumab, Rituximab | CD20 | Cytotoxicity (in vitro), IFNγ Release (in vitro), B-cell depletion (ex vivo) | NHL [54, 55] |
STRO-001 | CD74 | Cytotoxicity (in vitro) | NHL [56] |
Daratumumab | CD38 | Cytotoxicity (ex vivo, in vitro), NK Cell expansion (ex vivo) & activation (in vitro) | MM [57–59] |
Elotuzumab | SLAMF7 | Cytotoxicity (ex vivo), enzyme-linked Immunosorbent assay (ex vivo) | MM [60] |
Abbreviations: AML Acute myeloid leukemia, NHL Non-Hodgkin lymphoma, MM Multiple myeloma, NK Natural killer, IFN Interferon.
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