Classically speaking, assays for interactions between drug molecules and their targets have measured binding affinity under equilibrium conditions.
In drug discovery projects, it had long been assumed that carefully measuring IC50 (half-maximal inhibitory concentration), EC50 (effector concentration for half-maximal response), Kd (equilibrium dissociation constant), and/or Ki (inhibition constant) would allow drug candidates to be prioritized effectively for clinical studies.
However, an oft-cited and unfortunate statistic is that 90% of small-molecule drugs fail in the clinic due to unfavorable pharmacodynamic properties.
Drug-target residence time has been shown to be an important factor when characterizing new drugs. Could this help improve the success rate of new kinase inhibitors?
Could binding affinity and specificity be less relevant than initially assumed? Yes, says Robert Copeland. In 2006, Copeland and colleagues David Pompliano and Thomas Meek published the residence time model, which accounts for the conformational dynamics of target macromolecules that affect drug binding and dissociation.
Importantly, the residence time model postulates that the total lifetime (i.e., residence time) of the binary drug–target complex, not the binding affinity per se, dictates much of in vivo pharmacological activity. Drug-target residence time is calculated as the reciprocal of the dissociation constant, koff. In practice, residence time varies from second to days; the latter scenario leads to so-called durable pharmacodynamics, where residence time exceeds the pharmacokinetic half-life of the drug in systemic circulation—resulting in sustained drug activity even when the agent is “cleared” from the body.
The distinction between assays relying on thermodynamic equilibrium versus residence time is one of engineering versus biology. Equilibrium binding metrics pertain to in vitro assays run under closed system conditions, with invariant concentrations of drug and target throughout the duration of the experiment. This holds little relevance to physiological systems, Copeland asserts. Living organisms are open systems: concentrations of drug and target are in constant flux because of physiological processes like tissue and cellular distribution, gastrointestinal absorption, hepatic metabolism, and renal metabolism.
In short, biology is messy, and living systems encompass far more variables than reductionist models often include. Binding tightly or speedily matters little if, for example, metabolic or localization events mean that a drug and its target are rarely in proximity to one another.
Because residence time can be measured by the reciprocal of koff, a straightforward—and powerful—strategy is to allow drug and target to bind, then to monitor the disassociation of drug-target complex over time. This is readily done using a jump-dilution assay. Incubate the target enzyme with saturating amounts of inhibitor, form the complex, dilute the complex, and — voila — by continuously monitoring enzyme activity, you can calculate koff and residence time.
High-throughput screens (HTS) based on enzyme activity are relatively common, and for good reason. Reagents and instrumentation are readily available for key target families like kinases, ATPases, and phosphodiesterases. Take kinases, for example: with more than 500 human kinases, the 300 kinase inhibitors currently in clinical investigations represent a mere tip of the iceberg.
The Transcreener ADP2 Kinase Assay can be used to determine drug–kinase target residence times using the jump dilution method. In a study that measured residence times for multiple drugs that bind epidermal growth factor receptor (EGFR), ABL1, and Aurora kinases, the rank ordering of inhibitor koff values measured with the Transcreener assay correlated with literature values determined using ligand binding assays. Similar results were obtained regardless of detection mode: fluorescence polarization (FP), fluorescence intensity (FI), or time-resolved Förster resonance energy transfer (TR-FRET) all worked.
This flexibility and robustness matters for HTS drug discovery approaches. In a “fail fast, fail cheap” environment, relevance is key. Is a drug-target association pharmacodynamically meaningful? With the right assay, this question can be answered at an early stage. Armed with this information, drug discovery projects can progress with confidence to more advanced stages of testing—backed with clear determination of residence time.
– Robyn M. Perrin, PhD
 Hay, M.; Thomas, D. W.; Craighead, J. L.; et al. 2014. Clinical Development Success Rates for Investigational Drugs. Nat. Biotechnol. 32 (1): 40–51.
 Copeland RA, Pompliano DL, Meek TD. 2006. Drug–target residence time and its implications for lead optimization. Nature Rev Drug Disc. 5(9):730-9.
 Copeland RA. 2016. The drug-target residence time model: a 10-year retrospective. Nat Rev Drug Discov. 15(2):87-95.
 Kumar M, Lowery RG. 2017. A High-Throughput Method for Measuring Drug Residence Time Using the Transcreener ADP Assay. SLAS Discov. 22(7):915-922.
 Uitdehaag JCM, de Man J, Willemsen-Seegers N, Prinsen MBW, Libouban MAA, Sterrenburg JG, de Wit JJP, de Vetter JRF, de Roos JADM, Buijsman RC, Zaman GJ. 2017. Target Residence Time-Guided Optimization on TTK Kinase Results in Inhibitors with Potent Anti-Proliferative Activity. J Mol Biol. 429(14):2211-2230.