Damian W. Young's research while affiliated with Rice University and other places

Motivation Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy. Results As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7 min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making. Availability and implementation The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.

We report a heterocyclic merging approach to construct novel indazolo‐piperazines and indazolo‐morpholines. Starting from chiral diamines and amino alcohols, novel regiochemically (1,3 and 1,4) and stereochemically diverse (relative and absolute) cohorts of indazolo‐piperazines and indazolo‐morpholines were obtained within six or seven steps. The key transformations involved are a Smiles rearrangement to generate the indazole core structure and a late‐stage Michael addition to build the piperazine and morpholine heterocycles. We further explored additional vector diversity by incorporating substitutions on the indazole aromatic ring, generating a total of 20 unique, enantiomerically pure heterocyclic scaffolds.

The development of SARS-CoV-2 main protease (Mpro) inhibitors for the treatment of COVID-19 has mostly benefitted from X-ray structures and preexisting knowledge of inhibitors; however, an efficient method to generate Mpro inhibitors, which circumvents such information would be advantageous. As an alternative approach, we show here that DNA-encoded chemistry technology (DEC-Tec) can be used to discover inhibitors of Mpro. An affinity selection of a 4-billion-membered DNA-encoded chemical library (DECL) using Mpro as bait produces novel non-covalent and non-peptide-based small molecule inhibitors of Mpro with low nanomolar Ki values. Furthermore, these compounds demonstrate efficacy against mutant forms of Mpro that have shown resistance to the standard-of-care drug nirmatrelvir. Overall, this work demonstrates that DEC-Tec can efficiently generate novel and potent inhibitors without preliminary chemical or structural information.

Pexidartinib (PEX, TURALIO), a selective and potent inhibitor of the macrophage colony-stimulating factor-1 receptor, has been approved for the treatment of tenosynovial giant cell tumor. However, frequent and severe adverse effects have been reported in the clinic, resulting in a boxed warning on PEX for its risk of liver injury. The mechanisms underlying PEX-related hepatotoxicity, particularly metabolism-related toxicity, remain unknown. In the current study, the metabolic activation of PEX was investigated in human/mouse liver microsomes (HLM/MLM) and primary human hepatocytes (PHH) using glutathione (GSH) and methoxyamine (NH2OMe) as trapping reagents. A total of 11 PEX-GSH and 7 PEX-NH2OMe adducts were identified in HLM/MLM using an LC-MS-based metabolomics approach. Additionally, 4 PEX-GSH adducts were detected in the PHH. CYP3A4 and CYP3A5 were identified as the primary enzymes responsible for the formation of these adducts using recombinant human P450s and CYP3A chemical inhibitor ketoconazole. Overall, our studies suggested that PEX metabolism can produce reactive metabolites mediated by CYP3A, and the association of the reactive metabolites with PEX hepatotoxicity needs to be further studied.

Cytochrome P450-mediated carbon–carbon (C-C) cleavages are unusual, especially for mammalian drug metabolizing enzymes. Revealing the unusual reactions in biological system is very arduous and selectively oxidative C-C cleavage is also a long-standing challenge in chemistry and biology. We herein present a rapid and efficient metabolomic-based approach to uncover human CYP3A-mediated non-polar, unstrained C(sp2)-C(sp3) bond cleavage in the CSF-1R inhibitor pexidartinib. Using synthetic metabolites, 18O2, and H218O, we demonstrate that one unique cleavage is via the ipso-addition reaction. This is the first report of CYP3A-mediated ipso-addition reaction to the 5-alkylated N-protected pyridin-2-amines. We have expanded the range of substrates undergoing CYP3A-mediated ipso-addition reactions beyond para-phenols to include N-protected alkylated pyridine-2-amines. Our metabolomic-based approach also successfully discovered the CYP3A-mediated C(sp2)-C(sp3) bond cleavage of PEX analogs as well as the antidepressant nefazodone. This work established an efficient strategy to identify the uncommon reactions in drug metabolism using a metabolomic strategy. More importantly, the environmentally friendly conditions of CYP3A-catalyzed unusual ispo-addition reactions hold the potential to inspire future exploration of biomimetic P450-reprogramming methods for addressing the challenging task of unactivated C-C bond cleavage in the field.

Background: Activating somatic ESR1 mutations Y537S and D538G occur more frequently in endocrine therapy-resistant metastatic breast cancer, which is associated with an aggressive phenotype and poor survival in breast cancer patients. These gain of function mutant receptors are constitutively active and allow resistance to first-line endocrine therapies. Therefore, the development of next-generation small molecule drugs targeting mutant estrogen receptor (ER) is an important priority. Here, we searched the small molecule inhibitors for Y537S and D538D ER mutants using DNA-encoded chemical library screening. Methods: Wild type (WT) and mutant ER ligand binding domain (LBD) proteins were expressed in E. coli. The soluble proteins were purified by Ni-NTA chromatography followed by anion exchange and size exclusion chromatography. Homogeneous time-resolved fluorescence (HTRF) and fluorescent polarization (FP) assays were performed in these purified proteins. We employed a DNA-encoded chemical library affinity selection using our in-house collection of 6 billion compounds. Hit compounds were resynthesized and validated in biochemical assays. Finally, we have performed functional studies in CRISPR-Cas9 knock-in of Y537S and D538G mutant MCF-7 breast cancer cells. Results: We have successfully purified microgram amounts of ERα LBD of WT, Y537S, and D538G proteins. To test whether the purified WT and mutant proteins are active, HTRF and FP assays were performed in the presence and absence of estradiol and 4OH tamoxifen. Steroid receptor coactivator 3 (SRC3) peptide binding to the WT ER protein occurred only in the presence of estradiol. However, Y537S and D538G proteins are recruited by the SRC3 peptide in the absence of estradiol, indicating that these mutants are constitutively active and bind to SRC3. Furthermore, an in vitro biochemical FP assay was also established for WT and mutants in the presence of estradiol and 4OH tamoxifen. The screen of our multibillion small molecule collection of DNA-encoded chemical libraries identified several hits in WT and mutant ER. To confirm the selection output, we synthesized off-DNA compounds and validated these in biochemical and cell-based studies. We have identified that the compounds, CDD-1272 and CDD-1274, are active in HTRF and FP assays. Furthermore, these compounds inhibit WT and mutant cell growth in the presence of estradiol. More importantly, CDD-1274 degrades ER mutant and cyclin D1 proteins. In addition, CDD-1274 induced p21 protein expression in WT and mutant cells. Conclusions: We have identified potent novel ER mutant binders by using our DNA-encoded chemical library platform. Our compounds are active in biochemical and ER mutant cell lines, suggesting these molecules are potential chemical probes to explore in in vivo models of breast cancer. Support: NIH/NCI R03 CA259664 and CPRIT RP220524 to MP. Citation Format: Murugesan Palaniappan, Kurt M. Bohren, Yong Wang, Damian W. Young, Suzanne A. Fuqua, Martin M. Matzuk. Discovery and Development of Next-Generation Estrogen Receptor Mutant Inhibitors using DNA-Encoded Chemical Library Screening [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-10-12.

Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision-making.

The discovery of monokinase-selective inhibitors for patients is challenging because the 500+ kinases encoded by the human genome share highly conserved catalytic domains. Until now, no selective inhibitors unique for a single transforming growth factor β (TGFβ) family transmembrane receptor kinase, including bone morphogenetic protein receptor type 2 (BMPR2), have been reported. This dearth of receptor-specific kinase inhibitors hinders therapeutic options for skeletal defects and cancer as a result of an overactivated BMP signaling pathway. By screening 4.17 billion "unbiased" and "kinase-biased" DNA-encoded chemical library molecules, we identified hits CDD-1115 and CDD-1431, respectively, that were low-nanomolar selective kinase inhibitors of BMPR2. Structure-activity relationship studies addressed metabolic lability and high-molecular-weight issues, resulting in potent and BMPR2-selective inhibitor analogs CDD-1281 (IC50 = 1.2 nM) and CDD-1653 (IC50 = 2.8 nM), respectively. Our work demonstrates that DNA-encoded chemistry technology (DEC-Tec) is reliable for identifying novel first-in-class, highly potent, and selective kinase inhibitors.

Despite advances in drug discovery and development, treatments for neurological and mental health disorders still suffer from poor efficacy, high toxicity, and high attrition rates. This stems from intrinsic pitfalls of the current preclinical models used to test novel compounds. For example, animal models cannot replicate human genetics, the diversity of human brain cells, or certain aspects of human metabolism. The best models for drug development are those that can account for the effects that genetic and environmental diversity may have on basic tissue biology, at the same time being reproducible and accurately reflecting drug responses. Emerging technologies involving human pluripotent stem cells show great promise for developing such reliable, rapid, and cost-effective models to precisely reflect the diversity of human disease and responses to therapeutic agents that cannot be recapitulated in animals. Here we review some of these models and how they are starting to revolutionize the drug development pipeline.

Duloxetine (DLX) is widely used to treat major depressive disorder. Little is known about the mechanistic basis for DLX-related adverse effects (e.g., liver injury). Human CYP1A2 and CYP2D6 mainly contributes to DLX metabolism, which was proposed to be involved in its adverse effects. Here, we investigated the roles of Cyp1a2 and Cyp2d on DLX pharmacokinetic profile and tissue distribution using a Cyp1a2 knockout (Cyp1a2-KO) mouse model together with a Cyp2d inhibitor (propranolol). Cyp1a2-KO has the few effects on the systematic exposure (area under the plasma concentration–time curve, AUC) and tissue disposition of DLX and its primary metabolites. Propranolol dramatically increased the AUCs of DLX by 3 folds and 1.5 folds in WT and Cyp1a2-KO mice, respectively. Meanwhile, Cyp2d inhibitor decreased the AUC of Cyp2d-involved DLX metabolites (e.g., M16). Mouse tissue distribution revealed that DLX and its major metabolites were the most abundant in kidney, followed by liver and lung with/without Cyp2d inhibitor. Cyp2d inhibitor significantly increased DLX levels in tissues (e.g., liver) in WT and KO mice and decreases the levels of M3, M15, M16 and M17, while it increased the levels of M4, M28 and M29 in tissues. Our findings indicated that Cyp2d play a fundamental role on DLX pharmacokinetic profile and tissue distribution in mice. Clinical studies suggested that CYP1A2 has more effects on DLX systemic exposure than CYP2D6. Further studies in liver humanized mice or clinical studies concerning CYP2D6 inhibitors-DLX interaction study could clarify the roles of CYP2D6 on DLX pharmacokinetics and toxicity in human.