Discovery and Evaluation of Anti-Fibrinolytic Plasmin Inhibitors Derived from 5-(4-Piperidyl)isoxazol-3-ol (4-PIOL)

The slow but steady rise of binding free energy calculations in drug discovery

Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.

1,2,3-Triazole Derivatives as Novel Antifibrinolytic Drugs

Fibrinolysis is a natural process that ensures blood fluidity through the removal of fibrin deposits. However, excessive fibrinolytic activity can lead to complications in different circumstances, such as general surgery or severe trauma. The current antifibrinolytic drugs in the market, aminocaproic acid (EACA) and tranexamic acid (TXA), require high doses repetitively to maintain their therapeutic effect. These high doses are related to a number of side effects such as headaches, nasal symptoms, or gastrointestinal discomfort and severely limit their use in patients with renal impairment. Therefore, the discovery of novel antifibrinolytics with a higher specificity and lower dosage could vastly improve the applicability of these drugs. Herein, we synthesized a total of ten compounds consisting of a combination of three key moieties: an oxadiazolone, a triazole, and a terminal amine. The IC₅₀ of each compound was calculated in our clot lysis assays, and the best candidate (1) provided approximately a 2.5-fold improvement over the current gold standard, TXA. Molecular docking and molecular dynamics were used to perform a structure-activity relationship (SAR) analysis with the lysine binding site in the Kringle 1 domain of plasminogen. This analysis revealed that 1,2,3-triazole was crucial for the activity, enhancing the binding affinity through pi-pi stacking and polar interactions with Tyr72. The results presented in this work open the door to further investigate this new family as potential antifibrinolytic drugs.

Combining Monte Carlo and Molecular Dynamics Simulations for Enhanced Binding Free Energy Estimation through Markov State Models

We present a multistep protocol, combining Monte Carlo and molecular dynamics simulations, for the estimation of absolute binding free energies, one of the most significant challenges in computer-aided drug design. The protocol is based on an initial short enhanced Monte Carlo simulation, followed by clustering of the ligand positions, which serve to identify the most relevant states of the unbinding process. From these states, extensive molecular dynamics simulations are run to estimate an equilibrium probability distribution obtained with Markov State Models, which is subsequently used to estimate the binding free energy. We tested the procedure on two different protein systems, the Plasminogen kringle domain 1 and Urokinase, each with multiple ligands, for an aggregated molecular dynamics length of 760 μs. Our results indicate that the initial sampling of the unbinding events largely facilitates the convergence of the subsequent molecular dynamics exploration. Moreover, the protocol is capable to properly rank the set of ligands examined, albeit with a significant computational cost for the, more realistic, Urokinase complexes. Overall, this work demonstrates the usefulness of combining enhanced sampling methods with regular simulation techniques as a way to obtain more reliable binding affinity estimates.