Optimization of physicochemical properties for 4-anilinoquinazoline inhibitors of trypanosome proliferation

Abstract

Human African trypanosomiasis (HAT) is a deadly disease in need of new chemotherapeutics that can cross into the central nervous system. We previously reported the discovery of 2 (NEU-617), a small molecule with activity against T. brucei bloodstream proliferation. Further optimization of 2 to improve the physicochemical properties (LogP, LLE,[1] and MPO score)[2] have led us to twelve sub-micromolar compounds, most importantly the headgroup variants 9i and 9j, and the linker variant 18. Although these 3 compounds had reduced potency compared to 2, they all had improved LogP, LLE and MPO scores. Cross-screening these analogs against other protozoan parasites uncovered 9o with potent activity towards T. brucei, T. cruzi and L. major, while four others compounds (17, 18, 21, 26) showed activity towards P. falciparum D6. This reinforces the effectiveness of lead repurposing for the discovery of new protozoan disease therapeutics.

The efficacy of new 2,5-dihydroxybenzyl derivatives against Trypanosoma cruzi, Leishmania infantum and Leishmania braziliensis

“…parasites). The selection criteria for hit compounds used is based on the DnDi guidelines for Human African Trypanosomiasis and Visceral Leishmaniasis [46][47][48][49][50]:…”

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

“…The ZAP Toolkit has been widely used in the literature to calculate the electrostatic similarity score for two compounds (Boström et al, 2013;Tresadern et al, 2009;Chu and Gochin, 2013;Kim et al, 2015;Kossmann et al, 2016;Woodring et al, 2017;Maccari et al, 2011;Kim et al, 2016;López-Ramos and Perruccio, 2010;Hevener et al, 2012;Fig. 6.…”

“…VS applied to the electrostatic similarity of compounds is a clear example of this. Contrary to what happens when VS is applied to select the most similar compounds in shape or pharmacophore properties, where the tools base their predictions on scoring functions that measure these particular features (Lešnik et al, 2015;Puertas-Martín et al, 2019;Yan et al, 2013), the predictions in this field are not exclusively based on this descriptor, but on both the similarity of the three dimensional shape and electrostatic similarity (Tresadern et al, 2009;Chu and Gochin, 2013;Kim et al, 2015;Kossmann et al, 2016;Woodring et al, 2017;Maccari et al, 2011;Kim et al, 2016;López-Ramos and Perruccio, 2010;Hevener et al, 2012;Kaoud et al, 2012;Tiikkainen et al, 2009;Massarotti et al, 2014;Oyarzabal et al, 2009).…”

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

“…The ZAP Toolkit has been widely used in the literature to calculate the electrostatic similarity score for two compounds. 2,[15][16][17][18][19][20][21][22][23][24][25][26][27]50 In this subsection we would like to remark that the ZAP Toolkit can return an erroneous value, which was discovered when using Optipharm. During the optimization procedure, Optipharm can progressively separate two input compounds aimed to escape from local optima and explore the searching space in depth.…”

“…Contrary to what happens when VS is applied to select the most similar compounds in shape or pharmacophore properties, where the tools base their predictions on scoring functions that measure these particular features, 7,13,14 the predictions in this field are not exclusively based on this descriptor, but on both the similarity of the three dimensional shape and electrostatic similarity. [15][16][17][18][19][20][21][22][23][24][25][26][27] Broadly speaking, all the previous works follow the same methodology, although they may differ in the selection procedure used to determine the compounds proposed as best predictions. Essentially, they initially optimize the compounds in the database against the query in terms of shape by using ROCS, 28 they prioritize the top N compounds with the highest shape similarity values and then evaluate them in terms of electrostatic similarity.…”