Technological networks had the pleasure of speaking with Laura Heitman, Professor of Molecular Pharmacology in the Division of Drug Discovery and Safety at the Leiden Academic Center for Drug Research (LACDR), Leiden University, to learn more about his research focused on drug-target kinetics. Heitman explains why it’s important to determine how long a drug stays bound to its target, how you can assess a drug’s target binding kinetics, and how kinetic computer studies are helping advance the field.
Laura Lansdowne (LL): Can you tell us about your work focused on understanding and improving drug-receptor interactions?
: In general, my group’s research focuses on the theme “new receptor concepts to target membrane proteinsâWith the ultimate goal of improving the effectiveness of drugs. I selected membrane-bound proteins, such as G protein coupled receptors (GPCRs), because many drugs work through them and play a central role in the disease. Note that the concepts I am working on are in principle “disease independent” and can be applied to many targets and disease areas.
At the start of my term in January 2009, one of these concepts, namely “drug-target residence time” or “drug-target binding kinetics” had not received much attention, if any at all. all. Since then, it has been slowly realized that the time a drug remains bound to its target may be more important than affinity, in terms of the effect in the patient. Other articles are being published and describe the importance of optimizing the binding kinetics of a drug. However, few still report on this new parameter as a eventual tool, that is, to design compounds with optimal kinetics, rather than âstumblingâ on a compound with an interesting kinetic profile. Over the past few years, my group has developed several strong and accessible kinetic tests and started to publish this data.
Specifically, we were able to show for the first time that the binding kinetics of a drug on its target can be adjusted by a medicinal chemistry approach, alongside their affinity. This could have great clinical value, as retrospective analysis proves that some marketed drugs have clinical efficacy due to long target residence time. For example, using one of our in-house designed and synthesized products long stay (RT) CCR2 antagonists, we have shown that high receptor occupancy in a mouse model of atherosclerosis is the key to high efficacy. In particular, this high (or extended) occupation of the receptors leads to what is called insurmountable antagonism, i.e. antagonists which cannot be disturbed / thwarted by high local concentrations of the endogenous receptor agonist which is often causative of the disease state. Like a logic extension to the “long” target residence time, my group is currently also working on covalent ligands. As these molecules remain bound to their target ad infinitum (limited by the life cycle of the protein), the antagonists will be insurmountable.
LL: How to characterize the binding kinetics of a drug to its target?
LH: This can be done quantitatively and qualitatively depending on the method used. We tend to use radioligand binding assays to qualitatively assess the target binding kinetics of a drug. This can be done directly by radiolabelling or fluorescing the drug of interest, or indirectly by using a competitive association assay where a reference labeled ligand is used which then competes with an unlabeled ligand of interest. . In either case, analyzing the data by nonlinear regression models will give you the kinetic rate (kdisabled and kat) values.
With regard to the quantitative analysis, one can consider using wash assays where resistance to washout of the bond or some functional effect can be observed. In addition, in functional testing one can also assess the level of “insurmountability” of an antagonist, which is essentially a phenomenon that occurs when an antagonist occupies the target for an extended period of time, resulting in damping of the target. the maximum agonist response in this functional system. .
LL: Is there one experimental technique that you think has had the most impact?
LH: The introduction of “surface plasmon resonance” technology has really helped generate kinetic parameters in early drug discovery. Although some developments are underway, this technique is still not readily available or easily compatible with membrane-bound proteins.
LL: Why are the association and dissociation kinetics of a target-ligand complex so important?
LH: Despite efforts (and successes) to find selective, high affinity drug candidates, attrition rates in clinical trials are disappointing. New concepts such as drug-target binding kinetics are considered increasingly important for in vivo efficiency and safety. This is most likely true because the dynamic flow and metabolism in the human body often prevent drug molecules from reaching equilibrium conditions that are otherwise easily achieved in the test tube (i.e. that equilibrium parameters are still the norm in early drug discovery). In addition, in a disease state often different conditions occur at the target site, i.e. increased levels of endogenous agonist, as mentioned above. The kinetic behavior of the compounds (speed of association with the target and with metabolic enzymes, dissociation of the target, etc.) could indeed be the guiding principle to obtain a desired and lasting effect. in vivo. Therefore, it is important to better understand the drug-target interaction that is required and to optimize it at the molecular level. in vitro. Thus, offering the prospect of better chances for kinetically optimized drug candidates in later phases of the drug development process.
LL: How do advances in computer models influence our ability to explore binding kinetics?
LH: It is not my area of ââexpertise, but I would say that slowly more and more progress is being made in the field of kinetic computational studies. There are two computational techniques that can help understand and optimize drug-target binding kinetics: molecular dynamics (MD) and machine learning (ML). For both, ligand-protein structures are required, along with computational power (MD) and kinetic data (ML). Depending on the type of protein (i.e., membrane bound or cytosolic), structural data is limited, and kinetic data is currently also still scarce due to its underestimation. Once the limitations are lifted, these techniques can be used to visualize the molecular mode of target interaction, dissociation and possibly even association (MD), and aid in the prediction of binding kinetics for hit optimization. -lead (ML).
Laura Heitman spoke to Laura Elizabeth Lansdowne, editor-in-chief of Technology Networks.