scholarly journals An in vitro quantitative systems pharmacology approach for deconvolving mechanisms of drug-induced, multilineage cytopenias

2020 ◽  
Vol 16 (7) ◽  
pp. e1007620
Author(s):  
Jennifer L. Wilson ◽  
Dan Lu ◽  
Nick Corr ◽  
Aaron Fullerton ◽  
James Lu
Keyword(s):  
Systems Pharmacology ◽  
Drug Induced ◽  
Quantitative Systems Pharmacology

scholarly journals An in vitro quantitative systems pharmacology approach for deconvolving mechanisms of drug-induced, multilineage cytopenias

2019 ◽  
Author(s):  
Jennifer L. Wilson ◽  
Dan Lu ◽  
Nick Corr ◽  
Aaron Fullerton ◽  
James Lu
Keyword(s):  
Blood Cell ◽  
Drug Effects ◽  
Cell Killing ◽  
Cancer Therapies ◽  
Systems Pharmacology ◽  
Drug Induced ◽  
Quantitative Systems Pharmacology ◽  
Cell Lineages ◽  
Anti Cancer

AbstractMyelosuppression is one of the most common and severe adverse events associated with anti-cancer therapies and can be a source of drug attrition. Current mathematical modeling methods for assessing cytopenia risk rely on indirect measurements of drug effects and primarily focus on single lineage responses to drugs. However, anti-cancer therapies have diverse mechanisms with varying degrees of effect across hematopoietic lineages. To improve predictive understanding of drug-induced myelosuppression, we developed a quantitative systems pharmacology (QSP) model of hematopoiesis in vitro for quantifying the effects of anti-cancer agents on multiple hematopoietic cell lineages. We calibrated the system parameters of the model to cell kinetics data without treatment and then validated the model by showing that the inferred mechanisms of anti-proliferation and/or cell-killing are consistent with the published mechanisms for three classes of drugs with different mechanisms of action. Using a set of compounds as a sample set, we then analyzed novel compounds to predict their mechanisms and magnitude of myelosuppression. Further, these quantitative mechanisms are valuable for the development of translational in vivo models to predict clinical cytopenia effects.Author SummaryReduced bone marrow activity and levels of mature blood cells is an undesirable side effect of many anti-cancer therapies. Selecting promising lead compounds for further development requires understanding of potential myelosuppressive effects. However, existing preclinical experiments and modeling formulations fail to consider drug effects on multiple blood cell types or the mechanistic differences between how drugs induced myelosuppression. Here we developed a quantitative systems pharmacology (QSP) model that estimates a drug candidate’s effect on multiple precursor and mature blood cell lineages and further distinguishes how the drug affects these populations - through cell-killing or anti-proliferation mechanisms. This modeling formalism is valuable for vetting compounds for therapeutic development and for further translational modeling to anticipate the clinical effects of compounds.

scholarly journals Predicting In Vivo Efficacy from In Vitro Data: Quantitative Systems Pharmacology Modeling for an Epigenetic Modifier Drug in Cancer

2020 ◽  
Vol 13 (2) ◽  
pp. 419-429
Author(s):  
Mehdi Bouhaddou ◽  
Li J. Yu ◽  
Serena Lunardi ◽  
Spyros K. Stamatelos ◽  
Fiona Mack ◽  
...  
Keyword(s):  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
In Vivo Efficacy ◽  
Epigenetic Modifier ◽  
Vitro Data ◽  
In Vitro Data

scholarly journals Virtual Populations for Quantitative Systems Pharmacology Models

2021 ◽  
Author(s):  
Yougan Cheng ◽  
Ronny Straube ◽  
Abed Alnaif ◽  
Lu Huang ◽  
Tarek Leil ◽  
...  
Keyword(s):  
Clinical Data ◽  
Model Calibration ◽  
Algorithm Design ◽  
Computational Time ◽  
Systems Pharmacology ◽  
Drug Induced ◽  
Quantitative Systems Pharmacology ◽  
Clinical Biomarkers ◽  
Calibration Algorithm ◽  
Methodological Considerations

Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, auto-immune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.

A Hexagonal (II) Phase Phospholipid Neutralization Assay for Lupus Anticoagulant Identification

1993 ◽  
Vol 70 (05) ◽  
pp. 787-793 ◽  
Author(s):  
Douglas A Triplett ◽  
Linda K Barna ◽  
Gail A Unger
Keyword(s):  
Hemophilia A ◽  
Clinical Laboratory ◽  
Neutralization Assay ◽  
Confirmatory Test ◽  
Specific Factor ◽  
Clinical Settings ◽  
Drug Induced ◽  
Variable Screening ◽  
Routine Clinical Laboratory

SummaryLupus anticoagulants (LAs) are immunoglobulins (IgG, IgM, or both) which interfere with in vitro phospholipid (PL) dependent tests of coagulation (e.g. APTT, dilute PT, dilute Russell Viper Venom Time). These antibodies may be identified in a wide variety of clinical settings. With the exception of heparinized patient samples, the presence of LAs is often the most common cause of an unexplained APTT in a routine clinical laboratory. The diagnosis of LAs is difficult due to variable screening reagent sensitivity and intrinsic heterogeneity of LAs. Recently, Rauch and colleagues have shown human monoclonal hybridoma LAs were inhibited by hexagonal (II) phase PLs. In contrast, lamellar phase PLs had no effect. We have evaluated a new assay system, Staclot LA®, which utilizes a hexagonal (II) phase PL (egg phosphatidylethanolamine [EPE]) as a confirmatory test for LAs. Plasma samples from the following patient populations were studied: LA positive, heparinized, oral anticoagulated, hemophilia A and B, and specific factor inhibitors (factors V, VIII, IX). Unlike previous studies, the LA positive patients were a mixed population including: autoimmune diseases, drug-induced, and post-infection. Our findings confirm the specificity of hexagonal (II) phase PL neutralization of LAs.

Recent Progress in Prediction Systems for Drug-induced Liver Injury Using in vitro Cell Culture

2020 ◽  
Vol 14 ◽  
Author(s):  
Shogo Ozawa ◽  
Toshitaka Miura ◽  
Jun Terashima ◽  
Wataru Habano ◽  
Seiichi Ishida
Keyword(s):  
Cell Culture ◽  
Recent Progress ◽  
Inflammatory Cells ◽  
Protein Adducts ◽  
In Vitro Assays ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Culture Systems ◽  
Cell Culture Systems

Background: In order to avoid drug-induced liver injury (DILI), in vitro assays, which enable the assessment of both metabolic activation and immune reaction processes that ultimately result in DILI, are needed. Objective: In this study, the recent progress in the application of in vitro assays using cell culture systems is reviewed for potential DILI-causing drugs/xenobiotics and a mechanistic study on DILI, as well as for the limitations of in vitro cell culture systems for DILI research. Methods: Information related to DILI was collected through a literature search of the PubMed database. Results: The initial biological event for the onset of DILI is the formation of cellular protein adducts after drugs have been metabolically activated by drug metabolizing enzymes. The damaged peptides derived from protein adducts lead to the activation of CD4+ helper T lymphocytes and recognition by CD8+ cytotoxic T lymphocytes, which destroy hepatocytes through immunological reactions. Because DILI is a major cause of drug attrition and drug withdrawal, numerous in vitro systems consisting of hepatocytes and immune/inflammatory cells, or spheroids of human primary hepatocytes containing non-parenchymal cells have been developed. These cellular-based systems have identified DILIinducing drugs with approximately 50% sensitivity and 90% specificity. Conclusion: Different co-culture systems consisting of human hepatocyte-derived cells and other immune/inflammatory cells have enabled the identification of DILI-causing drugs and of the actual mechanisms of action.

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