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Translating slow-binding inhibition kinetics into cellular and in vivo effects

Abstract

Many drug candidates fail in clinical trials owing to a lack of efficacy from limited target engagement or an insufficient therapeutic index. Minimizing off-target effects while retaining the desired pharmacodynamic (PD) response can be achieved by reduced exposure for drugs that display kinetic selectivity in which the drug–target complex has a longer half-life than off-target–drug complexes. However, though slow-binding inhibition kinetics are a key feature of many marketed drugs, prospective tools that integrate drug-target residence time into predictions of drug efficacy are lacking, hindering the integration of drug-target kinetics into the drug discovery cascade. Here we describe a mechanistic PD model that includes drug-target kinetic parameters, including the on- and off-rates for the formation and breakdown of the drug–target complex. We demonstrate the utility of this model by using it to predict dose response curves for inhibitors of the LpxC enzyme from Pseudomonas aeruginosa in an animal model of infection.

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Figure 1
Figure 2: Rapid-dilution progress curves for P. aeruginosa LpxC inhibitors establish residence time.
Figure 3: P.aeruginosa PAO1 post-antibiotic effect data for representative LpxC inhibitors.
Figure 4: Derivation of a PD model incorporating time-dependent target inhibition parameters.
Figure 5: In vivo efficacy curves for 6 with mechanistic PD model fit.

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Acknowledgements

P.J.T. acknowledges this research was partially funded by the US National Institutes of Health (GM102864) and through a sponsored research agreement with AstraZeneca. We thank J. Kotz and R.A. Copeland for advice in preparation of the manuscript. We also acknowledge contributions from P. Hill, A. Li, T. Grebe and C. Joubran as well as S. Kuppusamy and the chemists at Syngene for chemical synthesis and analytical support; B. de Jonge for guidance on microbiological assessments; and K. Kapilashrami and A. Chang for helpful discussions.

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Authors

Contributions

E.K.H.A. designed experiments and performed PAE experiments. P.L.R. measured compound residence time and inhibition onset. Z.Y., S.L.F., V.J.A.S. and G.K.W. derived the integrated PD model. V.M., A.L.C. and K.M.-B. designed and synthesized compounds. S.A.P., E.T.B., L.H. and M.J. collected PAE data and determined resistance frequencies or MICs. L.H. prepared the lpxC overexpression strains. J.O'D. and L.A.G. designed and performed animal experiments. Z.Y. and D.E.E. performed data analysis of integrated equation models. S.G.W. oversaw PAE experiments. F.D. performed data analysis of differential equation models. M.J., G.K.W., Z.Y., P.L.R., E.K.H.A., M.R.H., J.O'D., D.E.E., P.J.T. and S.L.F. analyzed PAE and animal efficacy data and designed experiments. G.K.W., P.J.T. and S.L.F. wrote the manuscript.

Corresponding authors

Correspondence to Peter J Tonge or Stewart L Fisher.

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Competing interests

All authors except E.K.H.A., F.D., S.G.W. and P.J.T. were employees of AstraZeneca during the conduct of this research.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Figures 1–13, Supplementary Tables 1–12 and Supplementary Note (PDF 3060 kb)

Supplementary Data Set 1

Interactive PD model simulator (TXT 8 kb)

Supplementary Data Set 2

Cellular PAE PD model fitting tool (TXT 8 kb)

Supplementary Data Set 3

Pharmacokinetic PD model fitting tool (TXT 22 kb)

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Walkup, G., You, Z., Ross, P. et al. Translating slow-binding inhibition kinetics into cellular and in vivo effects. Nat Chem Biol 11, 416–423 (2015). https://doi.org/10.1038/nchembio.1796

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