The Immunogenomic Landscape of Peripheral High-Dose IL-2 Pharmacodynamics in Patients with Metastatic Renal Cell Carcinoma: A Benchmark for Next-Generation IL-2-Based Immunotherapies.

High-dose (HD) IL-2 was the first immuno-oncology agent approved for treating advanced renal cell carcinoma and metastatic melanoma, but its use was limited because of substantial toxicities. Multiple next-generation IL-2 agents are being developed to improve tolerability. However, a knowledge gap still exists for the genomic markers that define the target pharmacology for HD IL-2 itself. In this retrospective observational study, we collected PBMC samples from 23 patients with metastatic renal cell carcinoma who were treated with HD IL-2 between 2009 and 2015. We previously reported the results of flow cytometry analyses. In this study, we report the results of our RNA-sequencing immunogenomic survey, which was performed on bulk PBMC samples from immediately before (day 1), during (day 3), and after treatment (day 5) in cycle 1 and/or cycle 2 of the first course of HD IL-2. As part of a detailed analysis of immunogenomic response to HD IL-2 treatment, we analyzed the changes in individual genes and immune gene signatures. By day 3, most lymphoid cell types had transiently decreased, whereas myeloid transcripts increased. Although most genes and/or signatures generally returned to pretreatment expression levels by day 5, certain ones representative of B cell, NK cell, and T cell proliferation and effector functions continued to increase, along with B cell (but not T cell) oligoclonal expansion. Regulatory T cells progressively expanded during and after treatment. They showed strong negative correlation with myeloid effector cells. This detailed RNA-sequencing immunogenomic survey of IL-2 pharmacology complements results of prior flow cytometry analyses. These data provide valuable pharmacological context for assessing PBMC gene expression data from patients dosed with IL-2-related compounds that are currently in development.

Genome-wide scan identifies novel genetic loci regulating salivary metabolite levels.

Saliva, as a biofluid, is inexpensive and non-invasive to obtain, and provides a vital tool to investigate oral health and its interaction with systemic health conditions. There is growing interest in salivary biomarkers for systemic diseases, notably cardiovascular disease. Whereas hundreds of genetic loci have been shown to be involved in the regulation of blood metabolites, leading to significant insights into the pathogenesis of complex human diseases, little is known about the impact of host genetics on salivary metabolites. Here we report the first genome-wide association study exploring 476 salivary metabolites in 1419 subjects from the TwinsUK cohort (discovery phase), followed by replication in the Study of Health in Pomerania (SHIP-2) cohort. A total of 14 distinct locus-metabolite associations were identified in the discovery phase, most of which were replicated in SHIP-2. While only a limited number of the loci that are known to regulate blood metabolites were also associated with salivary metabolites in our study, we identified several novel saliva-specific locus-metabolite associations, including associations for the AGMAT (with the metabolites 4-guanidinobutanoate and beta-guanidinopropanoate), ATP13A5 (with the metabolite creatinine) and DPYS (with the metabolites 3-ureidopropionate and 3-ureidoisobutyrate) loci. Our study suggests that there may be regulatory pathways of particular relevance to the salivary metabolome. In addition, some of our findings may have clinical significance, such as the utility of the pyrimidine (uracil) degradation metabolites in predicting 5-fluorouracil toxicity and the role of the agmatine pathway metabolites as biomarkers of oral health.

Plasma metabolomic profiles enhance precision medicine for volunteers of normal health.

Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.

Human metabolic individuality in biomedical and pharmaceutical research.

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Application of metabolomics to diagnosis of insulin resistance.

Metabolomics, the global interrogation of the biochemical components in a biological sample, has become an important complement to genomics and proteomics to aid in the understanding of pathophysiology. Major advantages of metabolomics are the size of the metabolome relative to the genome or proteome and the fact that it provides a view of the existing biochemical phenotype. As such, metabolomics is fast becoming an important discovery tool for new diagnostic and prognostic biomarkers. Although many methods exist for performing metabolomics, relatively few have led to successful development of new diagnostic tests. This review will aid the reader in understanding various metabolomic methods and their applications, as well as some of their inherent advantages and disadvantages. In addition, we present one example of the application of metabolomics to the identification of new fasting blood biomarkers for the diagnosis and monitoring of insulin resistance.

Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism.

Global metabolic profiling currently achievable by untargeted mass spectrometry-based metabolomic platforms has great potential to advance our understanding of human disease states, including potential utility in the detection of novel and known inborn errors of metabolism (IEMs). There are few studies of the technical reproducibility, data analysis methods, and overall diagnostic capabilities when this technology is applied to clinical specimens for the diagnosis of IEMs. We explored the clinical utility of a metabolomic workflow capable of routinely generating semi-quantitative z-score values for ~900 unique compounds, including ~500 named human analytes, in a single analysis of human plasma. We tested the technical reproducibility of this platform and applied it to the retrospective diagnosis of 190 individual plasma samples, 120 of which were collected from patients with a confirmed IEM. Our results demonstrate high intra-assay precision and linear detection for the majority compounds tested. Individual metabolomic profiles provided excellent sensitivity and specificity for the detection of a wide range of metabolic disorders and identified novel biomarkers for some diseases. With this platform, it is possible to use one test to screen for dozens of IEMs that might otherwise require ordering multiple unique biochemical tests. However, this test may yield false negative results for certain disorders that would be detected by a more well-established quantitative test and in its current state should be considered a supplementary test. Our findings describe a novel approach to metabolomic analysis of clinical specimens and demonstrate the clinical utility of this technology for prospective screening of IEMs.

Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information.

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.

Metabolomic signatures of aggressive prostate cancer.

BACKGROUND: Current diagnostic techniques have increased the detection of prostate cancer; however, these tools inadequately stratify patients to minimize mortality. Recent studies have identified a biochemical signature of prostate cancer metastasis, including increased sarcosine abundance. This study examined the association of tissue metabolites with other clinically significant findings. METHODS: A state of the art metabolomics platform analyzed prostatectomy tissues (331 prostate tumor, 178 cancer-free prostate tissues) from two independent sites. Biochemicals were analyzed by gas chromatography-mass spectrometry and ultrahigh performance liquid chromatography-tandem mass spectrometry. Statistical analyses identified metabolites associated with cancer aggressiveness: Gleason score, extracapsular extension, and seminal vesicle and lymph node involvement. RESULTS: Prostate tumors had significantly altered metabolite profiles compared to cancer-free prostate tissues, including biochemicals associated with cell growth, energetics, stress, and loss of prostate-specific biochemistry. Many metabolites were further associated with clinical findings of aggressive disease. Aggressiveness-associated metabolites stratified prostate tumor tissues with high abundances of compounds associated with normal prostate function (e.g., citrate and polyamines) from more clinically advanced prostate tumors. These aggressive prostate tumors were further subdivided by abundance profiles of metabolites including NAD+ and kynurenine. When added to multiparametric nomograms, metabolites improved prediction of organ confinement (AUROC from 0.53 to 0.62) and 5-year recurrence (AUROC from 0.53 to 0.64). CONCLUSIONS: These findings support and extend earlier metabolomic studies in prostate cancer and studies where metabolic enzymes have been associated with carcinogenesis and/or outcome. Furthermore, these data suggest that panels of analytes may be valuable to translate metabolomic findings to clinically useful diagnostic tests.

Perturbation of bile acid homeostasis is an early pathogenesis event of drug induced liver injury in rats.

Drug-induced liver injury (DILI) is a significant consideration for drug development. Current preclinical DILI assessment relying on histopathology and clinical chemistry has limitations in sensitivity and discordance with human. To gain insights on DILI pathogenesis and identify potential biomarkers for improved DILI detection, we performed untargeted metabolomic analyses on rats treated with thirteen known hepatotoxins causing various types of DILI: necrosis (acetaminophen, bendazac, cyclosporine A, carbon tetrachloride, ethionine), cholestasis (methapyrilene and naphthylisothiocyanate), steatosis (tetracycline and ticlopidine), and idiosyncratic (carbamazepine, chlorzoxasone, flutamide, and nimesulide) at two doses and two time points. Statistical analysis and pathway mapping of the nearly 1900 metabolites profiled in the plasma, urine, and liver revealed diverse time and dose dependent metabolic cascades leading to DILI by the hepatotoxins. The most consistent change induced by the hepatotoxins, detectable even at the early time point/low dose, was the significant elevations of a panel of bile acids in the plasma and urine, suggesting that DILI impaired hepatic bile acid uptake from the circulation. Furthermore, bile acid amidation in the hepatocytes was altered depending on the severity of the hepatotoxin-induced oxidative stress. The alteration of the bile acids was most evident by the necrosis and cholestasis hepatotoxins, with more subtle effects by the steatosis and idiosyncratic hepatotoxins. Taking together, our data suggest that the perturbation of bile acid homeostasis is an early event of DILI. Upon further validation, selected bile acids in the circulation could be potentially used as sensitive and early DILI preclinical biomarkers.

Association of Antifolate Response Signature Status and Clinical Activity of Pemetrexed-Platinum Chemotherapy in Non-Small Cell Lung Cancer: The Piedmont Study.

PURPOSE: The Piedmont study is a prospectively designed retrospective evaluation of a new 48-gene antifolate response signature (AF-PRS) in patients with locally advanced/metastatic nonsquamous (NS) non-small cell lung cancer (NSCLC) treated with pemetrexed-containing platinum doublet chemotherapy (PMX-PDC). The study tested the hypothesis that AF-PRS identifies patients with NS-NSCLC who have a higher likelihood of responding positively to PMX-PDC. The goal was to gather clinical evidence supporting AF-PRS as a potential diagnostic test. EXPERIMENTAL DESIGN: Residual pretreatment FFPE tumor samples and clinical data were analyzed from 105 patients treated with first-line (1L) PMX-PDC. Ninety-five patients had sufficient RNA sequencing (RNA-seq) data quality and clinical annotation for inclusion in the analysis. Associations between AF-PRS status and associate genes and outcome measures including progression-free survival (PFS) and clinical response were evaluated. RESULTS: Overall, 53% of patients were AF-PRS(+), which was associated with extended PFS, but not overall survival, versus AF-PRS(-) (16.6 months vs. 6.6 months; P = 0.025). In patients who were stage I to III patients at the time of treatment, PFS was further extended in AF-PRS(+) versus AF-PRS(-) (36.2 months vs. 9.3 months; P = 0.03). Complete response (CR) to therapy was noted in 14 of 95 patients. AF-PRS(+) preferentially selected a majority (79%) of CRs, which were evenly split between patients stage I to III (six of seven) and stage IV (five of seven) at the time of treatment. CONCLUSIONS: AF-PRS identified a significant population of patients with extended PFS and/or clinical response following PMX-PDC treatment. AF-PRS may be a useful diagnostic test for patients indicated for systemic chemotherapy, especially when determining the optimal PDC regimen for locally advanced disease.

Biochemical alterations associated with ALS.

Our objective was to identify metabolic pathways affected by ALS using non-targeted metabolomics in plasma, comparing samples from healthy volunteers to those from ALS patients. This discovery could become the basis for the identification of therapeutic targets and diagnostic biomarkers of ALS. Two distinct cross-sectional studies were conducted. Plasma was collected from 62 (Study 1) and 99 (Study 2) participants meeting El Escorial criteria for possible, probable, or definite ALS; 69 (Study 1) and 48 (Study 2) healthy controls samples were collected. Global metabolic profiling was used to detect and evaluate biochemical signatures of ALS. Twenty-three metabolites were significantly altered in plasma from ALS patients in both studies. These metabolites include biochemicals in pathways associated with neuronal change, hypermetabolism, oxidative damage, and mitochondrial dysfunction, all of which are proposed disease mechanisms in ALS. The data also suggest possible hepatic dysfunction associated with ALS. In conclusion, the data presented here provide insight into the pathophysiology of ALS while suggesting promising areas of focus for future studies. The metabolomics approach can generate novel hypotheses regarding ALS disease mechanisms with the potential to identify therapeutic targets and novel diagnostic biomarkers.

Scaffold-based discovery of indeglitazar, a PPAR pan-active anti-diabetic agent.

In a search for more effective anti-diabetic treatment, we used a process coupling low-affinity biochemical screening with high-throughput co-crystallography in the design of a series of compounds that selectively modulate the activities of all three peroxisome proliferator-activated receptors (PPARs), PPARalpha, PPARgamma, and PPARdelta. Transcriptional transactivation assays were used to select compounds from this chemical series with a bias toward partial agonism toward PPARgamma, to circumvent the clinically observed side effects of full PPARgamma agonists. Co-crystallographic characterization of the lead molecule, indeglitazar, in complex with each of the 3 PPARs revealed the structural basis for its PPAR pan-activity and its partial agonistic response toward PPARgamma. Compared with full PPARgamma-agonists, indeglitazar is less potent in promoting adipocyte differentiation and only partially effective in stimulating adiponectin gene expression. Evaluation of the compound in vivo confirmed the reduced adiponectin response in animal models of obesity and diabetes while revealing strong beneficial effects on glucose, triglycerides, cholesterol, body weight, and other metabolic parameters. Indeglitazar has now progressed to Phase II clinical evaluations for Type 2 diabetes mellitus (T2DM).

Distinct Predictive Immunogenomic Profiles of Response to Immune Checkpoint Inhibitors and IL2: A Real-world Evidence Study of Patients with Advanced Renal Cancer.

Recombinant human high-dose IL2 (HD-IL2; aldesleukin) was one of the first approved immune-oncology agents based upon clinical activity in renal cell carcinoma (RCC) and metastatic melanoma but use was limited due to severe toxicity. Next-generation IL2 agents designed to improve tolerability are in development, increasing the need for future identification of genomic markers of clinical benefit and/or clinical response. In this retrospective study, we report clinical and tumor molecular profiling from patients with metastatic RCC (mRCC) treated with HD-IL2 and compare findings with patients with RCC treated with anti-PD-1 therapy. Genomic characteristics common and unique to IL2 and/or anti-PD-1 therapy response are presented, with insight into rational combination strategies for these agents. Residual pretreatment formalin-fixed paraffin embedded tumor samples from n = 36 patients with HD-IL2 mRCC underwent RNA-sequencing and corresponding clinical data were collected. A de novo 40-gene nearest centroid IL2 treatment response classifier and individual gene and/or immune marker signature differences were correlated to clinical response and placed into context with a separate dataset of n = 35 patients with anti-PD-1 mRCC. Immune signatures and genes, comprising suppressor and effector cells, were increased in patients with HD-IL2 clinical benefit. The 40-gene response classifier was also highly enriched for immune genes. While several effector immune signatures and genes were common between IL2 and anti-PD-1 treated patients, multiple inflammatory and/or immunosuppressive genes, previously reported to predict poor response to anti-PD-L1 immunotherapy, were only increased in IL2-responsive tumors. These findings suggest that common and distinct immune-related response markers for IL2 and anti-PD-1 therapy may help guide their use, either alone or in combination. Significance: Next-generation IL2 agents, designed for improved tolerability over traditional HD-IL2 (aldesleukin), are in clinical development. Retrospective molecular tumor profiling of patients treated with HD-IL2 or anti-PD-1 therapy provides insights into genomic characteristics of therapy response. This study revealed common and distinct immune-related predictive response markers for IL2 and anti-PD-1 therapy which may play a role in therapy guidance, and rational combination strategies for these agents.

Metabolomic profiling reveals biochemical pathways and biomarkers associated with pathogenesis in cystic fibrosis cells.

Cystic fibrosis (CF) is a life-shortening disease caused by a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. To gain an understanding of the epithelial dysfunction associated with CF mutations and discover biomarkers for therapeutics development, untargeted metabolomic analysis was performed on primary human airway epithelial cell cultures from three separate cohorts of CF patients and non-CF subjects. Statistical analysis revealed a set of reproducible and significant metabolic differences between the CF and non-CF cells. Aside from changes that were consistent with known CF effects, such as diminished cellular regulation against oxidative stress and osmotic stress, new observations on the cellular metabolism in the disease were generated. In the CF cells, the levels of various purine nucleotides, which may function to regulate cellular responses via purinergic signaling, were significantly decreased. Furthermore, CF cells exhibited reduced glucose metabolism in glycolysis, pentose phosphate pathway, and sorbitol pathway, which may further exacerbate oxidative stress and limit the epithelial cell response to environmental pressure. Taken together, these findings reveal novel metabolic abnormalities associated with the CF pathological process and identify a panel of potential biomarkers for therapeutic development using this model system.

Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists.

An investigative renal toxicity study using metabolomics was conducted with a potent nicotinic acid receptor (NAR) agonist, SCH 900424. Liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) techniques were used to identify small molecule biomarkers of acute kidney injury (AKI) that could aid in a better mechanistic understanding of SCH 900424-induced AKI in mice. The metabolomics study revealed 3-indoxyl sulfate (3IS) as a more sensitive marker of SCH 900424-induced renal toxicity than creatinine or urea. An LC-MS assay for quantitative determination of 3IS in mouse matrices was also developed. Following treatment with SCH 900424, 3IS levels were markedly increased in murine plasma and brain, thereby potentially contributing to renal- and central nervous system (CNS)-related rapid onset of toxicities. Furthermore, significant decrease in urinary excretion of 3IS in those animals due to compromised renal function may be associated with the elevation of 3IS in plasma and brain. These data suggest that 3IS has a potential to be a marker of renal and CNS toxicities during chemically-induced AKI in mice. In addition, based on the metabolomic analysis other statistically significant plasma markers including p-cresol-sulfate and tryptophan catabolites (kynurenate, kynurenine, 3-indole-lactate) might be of toxicological importance but have not been studied in detail. This comprehensive approach that includes untargeted metabolomic and targeted bioanalytical sample analyses could be used to investigate toxicity of other compounds that pose preclinical or clinical development challenges in a pharmaceutical discovery and development.

Metabolomic profiling can predict which humans will develop liver dysfunction when deprived of dietary choline.

Choline is an essential nutrient, and deficiency causes liver and muscle dysfunction. Common genetic variations alter the risk of developing organ dysfunction when choline deficient, probably by causing metabolic inefficiencies that should be detectable even while ingesting a normal choline-adequate diet. We determined whether metabolomic profiling of plasma at baseline could predict whether humans will develop liver dysfunction when deprived of dietary choline. Fifty-three participants were fed a diet containing 550 mg choline/70 kg/d for 10 d and then fed < 50 mg choline/70 kg/d for up to 42 d. Participants who developed organ dysfunction on this diet were repleted with a choline-adequate diet for > or = 3 d. Plasma samples, obtained at baseline, end of depletion, and end of repletion, were used for targeted and nontargeted metabolomic profiling. Liver fat was assessed using magnetic resonance spectroscopy. Metabolomic profiling and targeted biochemical analyses were highly correlated for the analytes assessed by both procedures. In addition, we report relative concentration changes of other small molecules detected by the nontargeted metabolomic analysis after choline depletion. Finally, we show that metabolomic profiles of participants when they were consuming a control baseline diet could predict whether they would develop liver dysfunction when deprived of dietary choline.

Untargeted metabolomic profiling as an evaluative tool of fenofibrate-induced toxicology in Fischer 344 male rats.

Peroxisome proliferator-activated receptor-alpha (PPARalpha) agonists such as fenofibrate are used to treat dyslipidemia. Although fenofibrate is considered safe in humans, it is known to cause hepatocarcinogenesis in rodents. To evaluate untargeted metabolic profiling as a tool for gaining insight into the underlying pharmacology and hepatotoxicology, Fischer 344 male rats were dosed with 300 mg/kg/day of fenofibrate for 14 days and the urine and plasma were analyzed on days 2 and 14. A combination of liquid and gas chromatography mass spectrometry returned the profiles of 486 plasma and 932 urinary metabolites. Aside from known pharmacological effects, such as accelerated fatty acid beta-oxidation and reduced plasma cholesterol, new observations on the drug's impact on cellular metabolism were generated. Reductions in TCA cycle intermediates and biochemical evidence of lactic acidosis demonstrated that energy metabolism homeostasis was altered. Perturbation of the glutathione biosynthesis and elevation of oxidative stress markers were observed. Furthermore, tryptophan metabolism was up-regulated, resulting in accumulation of tryptophan metabolites associated with reactive oxygen species generation, suggesting the possibility of oxidative stress as a mechanism of nongenotoxic carcinogenesis. Finally, several metabolites related to liver function, kidney function, cell damage, and cell proliferation were altered by fenofibrate-induced toxicity at this dose.

A family of phosphodiesterase inhibitors discovered by cocrystallography and scaffold-based drug design.

Cyclic nucleotide phosphodiesterases (PDEs) comprise a large family of enzymes that regulate a variety of cellular processes. We describe a family of potent PDE4 inhibitors discovered using an efficient method for scaffold-based drug design. This method involves an iterative approach starting with low-affinity screening of compounds followed by high-throughput cocrystallography to reveal the molecular basis underlying the activity of the newly identified compounds. Through detailed structural analysis of the interaction of the initially discovered pyrazole carboxylic ester scaffold with PDE4D using X-ray crystallography, we identified three sites of chemical substitution and designed small selective libraries of scaffold derivatives with modifications at these sites. A 4,000-fold increase in the potency of this PDE4 inhibitor was achieved after only two rounds of chemical synthesis and the structural analysis of seven pyrazole derivatives bound to PDE4B or PDE4D, revealing the robustness of this approach for identifying new inhibitors that can be further developed into drug candidates.

Structural basis for the activity of drugs that inhibit phosphodiesterases.

Phosphodiesterases (PDEs) comprise a large family of enzymes that catalyze the hydrolysis of cAMP or cGMP and are implicated in various diseases. We describe the high-resolution crystal structures of the catalytic domains of PDE4B, PDE4D, and PDE5A with ten different inhibitors, including the drug candidates cilomilast and roflumilast, for respiratory diseases. These cocrystal structures reveal a common scheme of inhibitor binding to the PDEs: (i) a hydrophobic clamp formed by highly conserved hydrophobic residues that sandwich the inhibitor in the active site; (ii) hydrogen bonding to an invariant glutamine that controls the orientation of inhibitor binding. A scaffold can be readily identified for any given inhibitor based on the formation of these two types of conserved interactions. These structural insights will enable the design of isoform-selective inhibitors with improved binding affinity and should facilitate the discovery of more potent and selective PDE inhibitors for the treatment of a variety of diseases.