Design and Synthesis of Poly(ADP-ribose) Polymerase Inhibitors: Impact of Adenosine Pocket-Binding Motif Appendage to the 3-Oxo-2,3-dihydrobenzofuran-7-carboxamide on Potency and Selectivity

Abstract

To evaluate interrater agreement in categorizing treatment outcomes and drug responsiveness status according to the International League Against Epilepsy (ILAE) definition of drug-resistant epilepsy. Methods: A total of 1053 adults with focal epilepsy considered by the investigators to meet ILAE criteria for drug resistance were enrolled consecutively at 43 centers and followed up prospectively for 18-34 months. Treatment outcomes for all antiepileptic drugs (AEDs) used up to enrollment (retrospective assessment), and on an AED newly introduced at enrollment, were categorized by individual investigators and by 2 rotating members of a 16-member expert panel (EP) that reviewed the patient records independently. Interrater agreement was tested by Cohen's kappa (k) statistics and rated according to Landis and Koch's criteria. Results: Agreement between EP members in categorizing outcomes on the newly introduced AED was almost perfect (90.1%, k = 0.84, 95% confidence interval [CI] 0.80-0.87), whereas agreement between the EP and individual investigators was moderate (70.4%, k = 0.57, 95% CI 0.53-0.61). Similarly, categorization of outcomes on previously used AEDs was almost perfect between EP members (91.7%, k = 0.83, 95% CI 0.81-0.84) and moderate between the EP and investigators (68.2%, k = 0.50, 95% CI 0.48-0.52). Disagreement was related predominantly to outcomes considered to be treatment failures by the investigators but categorized as undetermined by the EP. Overall, 19% of patients classified as having drug-resistant epilepsy by the investigators were considered by the EP to have "undefined responsiveness."Significance: Interrater agreement in categorizing treatment outcomes according to ILAE criteria ranges from moderate to almost perfect. Nearly 1 in 5 patients considered by enrolling neurologists to be "drug-resistant" were classified by the EP as having "undefined responsiveness."

Dynamics of the HD regulatory subdomain of PARP-1; substrate access and allostery in PARP activation and inhibition

“…Such inhibitor-induced changes would also be expected to be relevant to understanding activation. The great majority of PARP inhibitors mimic only the nicotinamide moiety of PARP’s natural substrate NAD + , and it has previously been shown that a non-hydrolysable analogue of NAD + (benzamide adenine dinucleotide, BAD), which includes also the adenosine moiety, is incapable of binding to isolated PARP-1 CAT domain unless the HD subdomain is deleted ( 19 ); in addition, a series of inhibitors designed to reach into the adenosine binding pocket were all similarly unable to bind to the isolated, intact CAT domain ( 20 ). In light of such observations, the autoinhibitory blockade of catalysis caused by the HD subdomain, and which is relieved during activation, is hypothesised to be primarily a steric barrier to binding of the adenosine moiety of NAD + .…”

AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization

“…5). We note that the crystallographic poses of several other PARPis position piperazine moieties at alternate locations [19,[66][67][68]. For example, the crystal structure of the potent PARPi EB 47 [69,70] bound to PARP16 (PDB ID: 6HXR) places a piperazine substructure near that of our compound 5 docked pose.…”

“…5c). These interactions are typical of the binding modes of known PARPi [18][19][20][58][59][60][61][62][63][64][65][66] such as the crystallographic olaparib pose (Fig. 5a) [19] and a docked CEP-9722 pose (Cephalon, Fig.…”

“…These results reinforce the critical role Y907 plays in high-affinity binding. Many known co-crystallized PARPi participate in a π-π stacking interaction with Y907 [18][19][20][58][59][60][61][62][63][64][65][66]. The top-100 compounds (per the docking score) produced in both the large-scale de novo and lead-optimization runs are all predicted to interact with Y907, suggesting that this interaction may be broadly critical regardless of the chemical scaffold.…”

Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors

“…Moreover, SA and QED scores of A_sample1 (0.65 and 0.56 respectively) are better than those of Compound 3 (0.54 and 0.51). Visual inspection reveals that A_sample1 conserves the π-π stacking interaction with Y907 and Y896 and the hydrogen bond with G863 that are well known binding interactions [29][30][31][32][33][34][35][36] . In addition, the interacting residues H862 and Y896 are well conserved residues from PARP-1 to PARP-6 37 .…”