Nestor, M. | Search bioRxiv

bioRxiv2023

Design-Rules for Stapled Alpha-Helical Peptides with On-Target In Vivo Activity: Application toMdm2/X dual antagonists

Stapled -helical peptides can bind to and modulate historically intractable targets while addressing the traditional liabilities associated with peptide therapeutics. However, their pipeline advancement has been impeded by the challenges of identifying peptides with sufficient cellular uptake to engage the target protein while lacking off-target toxicities. Here, we advance the field to arrive at a workflow for identifying advanced stapled peptide lead molecules with on-target in vivo activity with no off-target cell proliferation effects. Specifically, we generated a >350-member library based on ATSP-7041, a stapled peptide Mdm2(X) antagonist with validated on-target cellular effects but with significant off-target activity. Key insights from library analysis include 1) a clear correlation between lipophilicity and permeability, 2) removal of positive charge to avoid off-target toxicities, 3) judicious placement of anionic residues to enhance peptide solubility/behavior, 4) optimization of C-terminal length and helicity to enhance cell activity, 5) optimization of staple type/number to avoid polypharmacology. Incorporation of one or more of these attributes led to molecules with improved in vitro and in vivo activities (up to a >292x improved cell proliferation EC50). A subset of peptides were devoid of off-target cell proliferation effects in cell lines lacking wild-type p53 protein (up to a >3800x on-target index). This latter improvement contrasted with clinical Mdm2 antagonistic molecules. Application of these design rules to a distinct Mdm2(X) peptide series resulted in rapid improvement in cellular activity (>150x) and removal of off-target toxicities. Overall, the detailed workflow outlined here should help researchers identify stapled -helical peptides for therapeutic impact.

biochemistry↗

bioRxiv2021

Utilizing proteomics and phosphoproteomics to predict ex vivo drug sensitivity across genetically diverse AML patients

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemia. This genetic heterogeneity is difficult to treat using conventional therapies that are generally based on the detection of a single driving mutation. Thus, the use of molecular signatures, consisting of multiple functionally related transcripts or proteins, in making treatment decisions may overcome this hurdle and provide a more effective way to inform drug treatment protocols. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify signatures that could predict patient-specific drug responses. The Clinical Proteomic Tumor Analysis Consortium is in the process of extending this cohort to collect proteomic and phosphoproteomic measurements from a subset of these patient samples to evaluate the hypothesis that proteomic signatures can robustly predict drug response in AML patients. We sought to examine this hypothesis on a sub-cohort of 38 patient samples from Beat AML with proteomic and drug response data and evaluate our ability to identify proteomic signatures that predict drug response with high accuracy. For this initial analysis we built predictive models of patient drug responses across 26 drugs of interest using the proteomics and phosphproteomics data. We found that proteomics-derived signatures provide an accurate and robust signature of drug response in the AML ex vivo samples, as well as related cell lines, with better performance than those signatures derived from mutations or mRNA expression. Furthermore, we found that in specific drug-resistant cell lines, the proteins in our prognostic signatures represented dysregulated signaling pathways compared to parental cell lines, confirming the role of the proteins in the signatures in drug resistance. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to predict drug sensitivity in AML.

bioinformatics↗