Abstract:A major challenge in oncology drug development is to elucidate why drugs that show promising results in cancer cell lines in vitro fail in mouse studies or human trials.
“…These metrics correct for differences in growth rates among cell lines, which can otherwise confound interpretations of drug potency. Recent publications showcase the added value of using growth rate metrics for predicting in vivo responses from in vitro data alone [5] and support the further elaboration of this approach for drug development.…”
Section: Introductionmentioning
confidence: 77%
“…Second, the Free Drug Hypothesis is generally taken prima facie when translating drug PD from in vitro systems into living organisms [5], [8]. This draws from the assumption that drug nonspecifically bound to circulating plasma protein or extracellular parenchymal protein is unable to interact with its intended target -in this case, the intracellular kinase domain of HER2.…”
Background and Purpose: Despite their use to treat cancers with specific
genetic aberrations, targeted therapies elicit heterogeneous responses.
Sources of variability are critical to targeted therapy drug
development, yet there exists no method to discern their relative
contribution to response heterogeneity. Experimental Approach: We use
HER2-amplified breast cancer and two agents, neratinib and lapatinib, to
develop a platform for dissecting sources of variability in patient
response. The platform comprises four components: pharmacokinetics,
tumor burden and growth kinetics, clonal composition, and sensitivity to
treatment. Pharmacokinetics are simulated using population models to
capture variable systemic exposure. Tumor burden and growth kinetics are
derived from clinical data comprising over 800,000 women. The fraction
of sensitive and resistant tumor cells is informed by HER2
immunohistochemistry. Growth rate-corrected drug potency is used to
predict response. We integrate these factors and simulate clinical
outcomes for virtual patients. The relative contribution of these
factors to response heterogeneity are compared. Key Results: The
platform was verified with clinical data, including response rate and
progression-free survival (PFS). For both neratinib and lapatinib, the
growth rate of resistant clones influenced PFS to a higher degree than
systemic drug exposure. Variability in exposure at labeled doses did not
significantly influence response. Drug potency strongly influenced
responses to neratinib. Variability in patient HER2 immunohistochemistry
scores influenced responses to lapatinib. Exploratory twice daily dosing
improved PFS for neratinib but not lapatinib. Conclusion and
Implications: The platform can dissect sources of variability in
response to target therapy, which may facilitate decision-making during
drug development.
“…These metrics correct for differences in growth rates among cell lines, which can otherwise confound interpretations of drug potency. Recent publications showcase the added value of using growth rate metrics for predicting in vivo responses from in vitro data alone [5] and support the further elaboration of this approach for drug development.…”
Section: Introductionmentioning
confidence: 77%
“…Second, the Free Drug Hypothesis is generally taken prima facie when translating drug PD from in vitro systems into living organisms [5], [8]. This draws from the assumption that drug nonspecifically bound to circulating plasma protein or extracellular parenchymal protein is unable to interact with its intended target -in this case, the intracellular kinase domain of HER2.…”
Background and Purpose: Despite their use to treat cancers with specific
genetic aberrations, targeted therapies elicit heterogeneous responses.
Sources of variability are critical to targeted therapy drug
development, yet there exists no method to discern their relative
contribution to response heterogeneity. Experimental Approach: We use
HER2-amplified breast cancer and two agents, neratinib and lapatinib, to
develop a platform for dissecting sources of variability in patient
response. The platform comprises four components: pharmacokinetics,
tumor burden and growth kinetics, clonal composition, and sensitivity to
treatment. Pharmacokinetics are simulated using population models to
capture variable systemic exposure. Tumor burden and growth kinetics are
derived from clinical data comprising over 800,000 women. The fraction
of sensitive and resistant tumor cells is informed by HER2
immunohistochemistry. Growth rate-corrected drug potency is used to
predict response. We integrate these factors and simulate clinical
outcomes for virtual patients. The relative contribution of these
factors to response heterogeneity are compared. Key Results: The
platform was verified with clinical data, including response rate and
progression-free survival (PFS). For both neratinib and lapatinib, the
growth rate of resistant clones influenced PFS to a higher degree than
systemic drug exposure. Variability in exposure at labeled doses did not
significantly influence response. Drug potency strongly influenced
responses to neratinib. Variability in patient HER2 immunohistochemistry
scores influenced responses to lapatinib. Exploratory twice daily dosing
improved PFS for neratinib but not lapatinib. Conclusion and
Implications: The platform can dissect sources of variability in
response to target therapy, which may facilitate decision-making during
drug development.
“…In the context of RV-based analysis of drug sensitivity, a critical issue is the confounding influence of growth rate variation, particularly when comparisons are made between cell types with different growth rates 26,42 . The confounding influence of growth rate variation can also clearly be observed in our population fold change-based chemo-genetic profile.…”
DNA damage can activate apoptotic and non-apoptotic forms of cell death; however, it remains unclear what features dictate which type of cell death is activated. We report that p53 controls the choice between apoptotic and non-apoptotic death following exposure to lethal levels of DNA damage. The canonical response to DNA damage involves p53-dependent activation of cell intrinsic apoptosis, downstream of DNA damage response (DDR) activation. Decades of research suggest that DNA damage does not robustly activate cell death in the absence of p53. In contrast, we find that p53-deficient cells die at high rates following exposure to DNA damage, but exclusively using non-apoptotic types of cell death. Our experimental and computational analyses demonstrate that non-apoptotic death in p53-deficient cells has generally been missed due to use of assays that are either insensitive to cell death, or that specifically measure apoptotic cells. To characterize which subtype of non-apoptotic death is activated by DNA damage in p53-deficient cells, we used functional genetic screening, with an analysis method that enables computational inference of the drug-induced death rate, rather than the relative population size. We find in p53-deficient cells that DNA damage activates a mitochondrial respiration-dependent form of cell death called MPT-driven necrosis. This study reveals how the dual functions of p53 in regulating mitochondrial activity and the DDR combine to facilitate choice between apoptotic and non-apoptotic death following DNA damage.
“…This independency is the assumption of almost all modelling efforts for xenograft studies at Boehringer Ingelheim. We further illustrate this here by an external data set published by Genentech [ 28 ]. Our analysis of their data firstly demonstrates that a similar IVIVC analysis for their compounds can be given as for ours.…”
Section: Appendix a Illustration Of Separation Of
In-vi...mentioning
confidence: 99%
“… Fig. 7 IVIVC relationship for the study [ 28 ] as reanalyzed by us. Shapes are different compounds, color indicate different doses.…”
Section: Appendix a Illustration Of Separation Of
In-vi...mentioning
In-vitro to in-vivo correlations (IVIVC), relating in-vitro parameters like IC50 to in-vivo drug exposure in plasma and tumour growth, are widely used in oncology for experimental design and dose decisions. However, they lack a deeper understanding of the underlying mechanisms. Our paper therefore focuses on linking empirical IVIVC relations for small-molecule kinase inhibitors with a semi-mechanistic tumour-growth model. We develop an approach incorporating parameters like the compound’s peak-trough ratio (PTR), Hill coefficient of in-vitro dose-response curves, and xenograft-specific properties. This leads to formulas for determining efficacious doses for tumor stasis under linear pharmacokinetics equivalent to traditional empirical IVIVC relations, but enabling more systematic analysis. Our findings reveal that in-vivo xenograft-specific parameters, specifically the growth rate (g) and decay rate (d), along with the average exposure, are generally more significant determinants of tumor stasis and effective dose than the compound’s peak-trough ratio. However, as the Hill coefficient increases, the dependency of tumor stasis on the PTR becomes more pronounced, indicating that the compound is more influenced by its maximum or trough values rather than the average exposure. Furthermore, we discuss the translation of our method to predict population dose ranges in clinical studies and propose a resistance mechanism that solely relies on specific in-vivo xenograft parameters instead of IC50 exposure coverage. In summary, our study aims to provide a more mechanistic understanding of IVIVC relations, emphasizing the importance of xenograft-specific parameters and PTR on tumor stasis.
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