BIreactive: Expanding the Scope of Reactivity Predictions to Propynamides

Abstract

We present the first comprehensive study on the prediction of reactivity for propynamides. Covalent inhibitors like propynamides often show improved potency, selectivity, and unique pharmacologic properties compared to their non-covalent counterparts. In order to achieve this, it is essential to tune the reactivity of the warhead. This study shows how three different in silico methods can predict the in vitro properties of propynamides, a covalent warhead class integrated into approved drugs on the market. Whereas the electrophilicity index is only applicable to individual subclasses of substitutions, adduct formation and transition state energies have a good predictability for the in vitro reactivity with glutathione (GSH). In summary, the reported methods are well suited to estimate the reactivity of propynamides. With this knowledge, the fine tuning of the reactivity is possible which leads to a speed up of the design process of covalent drugs.

Keywords: covalent warheads; drug discovery; glutathione; propynamide; reactivity assessment; targeted covalent inhibitors.

Figure 1

Selected approved drugs that are on the market with covalent mode of action identified after product launch (aspirin (I), penicillin G (II), and omeprazole (III) [18,37]). Selected targeted covalent inhibitors (TCIs) on the market designed to covalently interact with target protein (osimertinib (IV) [21], ibrutinib (V) [22], ritlecitinib (VI) [25], tirabrutinib (VII) [23], acalabrutinib (VIII) [24]), and in phase II clinical trials (branebrutinib (IX) [6]).

Scheme 1

A two-step mechanism of covalent inhibition. E = enzyme, I = covalent inhibitor, E*I = non-covalent complex, and E-I = covalent complex. While K_i depends on non-covalent interactions, k_inact is influenced by intrinsic compound reactivity.

Figure 2

The energetically most favored transition state found in this study. The relevant LUMO orbitals are shown in Figure S2.

Figure 3

Benchmark molecules used for reactivity prediction of propynamide variations. The variations are divided into a right-hand side with different warheads (1–4) and a left-hand side with different non-covalent scaffolds (a–g).

Figure 4

Calculation for the benchmark set with the combination of different warheads and scaffolds: (A) the result of the electrophilicity index (R²: 0.38), (B) the result of the adduct formation energy (R²: 0.81), (C) the result for the transition state energy (R²: 0.86), and (D) the correlation between transition state energy and adduct formation energy. (R²: 0.76, MAE: 0.91 kcal/mol). Four exemplary structures are shown.

Figure 5

Calculation for the whole dataset with the combination of different warheads and scaffolds: (A) the result of the electrophilicity index (R²: 0.47), (B) the result of the adduct formation energy (R²: 0.82), (C) the result for the transition state energy (R²: 0.86), and (D) the correlation between transition state energy and adduct formation energy. (R²: 0.73, MAE: 0.61 kcal/mol).

Figure 6

Example molecules to further test the influence of a warhead substitution pattern.

Figure 7

Calculated transition states and adduct formation energies for the seven new warhead substituted molecules with the molecules from the benchmark set 1d–4d.