Generation of a Backup Clinical Candidate for (+)-SJ733

The overall goal of this research is to nominate a backup clinical candidate for the antimalarial (+)-SJ733 that possesses improved human pharmacokinetics (PK). Over the last 10 years, the Guy lab and its collaborators have screened more than 5 million compounds in cellular phenotypic high throughput assays against Plasmodium falciparum (P.fal.), the organism that causes the most lethal form of human malaria. Pharmacologic optimization of one of these series, the dihydroisoquinolones (DHIQs), led to the identification of (+)-SJ733, which acts upon PfATP4. (+)-SJ733 is currently in early clinical development and is well tolerated and efficacious. The only noted liability has been oxidative metabolism of (+)-SJ733 that leads to formation of an inactive metabolite. Herein, we propose to identify a new DHIQ analog that maintains the in vivo potency, novel mechanism of action, and low toxicity of (+)-SJ733 but possesses . 4-fold improvement in metabolic stability and human pharmacokinetics. The extensive investigation of the DHIQ class of PfATP4 inhibitors, which resulted in the identification of (+)-SJ733, provides an extensive database from which to initiate the search for an improved backup compound. Based on the available SAR and SPR data (~400 DHIQ analogs), modifications to the A-ring represent the best opportunity to meet our goals. Our overall strategy uses a recursive process including sequential hypothesis based design of analog sets and testing of those sets in models of increasing complexity and stringency. Our previous work has demonstrated a strong correlation between in vitro and in vivo efficacy and clearance for the DHIQfs. Thus, we can confidently prioritize and cull compounds based largely on our in vitro models for potency and metabolic stability. Optimized compounds will be evaluated in a murine efficacy model that best recapitulates the human disease. Only compounds that are superior to (+)-SJ733 will be nominated for subsequent studies to generate a full pre-clincial data set.