Patent Foramen Ovale Closure in Older Patients With Stroke: Patient Selection for Trial Feasibility.

BACKGROUND AND OBJECTIVES: Whether patent foramen ovale (PFO) closure benefits older patients with PFO and cryptogenic stroke is unknown because randomized controlled trials (RCTs) have predominantly enrolled patients younger than 60 years of age. Our objective was to estimate anticipated effects of PFO closure in older patients to predict the numbers needed to plan an RCT. METHODS: Effectiveness estimates are derived from major observational studies (Risk of Paradoxical Embolism [RoPE] Study and Oxford Vascular Study, together referred to as the "RoPE-Ox" database) and all 6 major RCTs (Systematic, Collaborative, PFO Closure Evaluation [SCOPE] Consortium). To estimate stroke recurrence risk, observed outcomes were calculated for patients older than 60 years in the age-inclusive observational databases (n = 549). To estimate the reduction in the rate of recurrent stroke associated with PFO closure vs medical therapy based on the RoPE score and the presence of high-risk PFO features, a Cox proportional hazards regression model was developed on the RCT data in the SCOPE database (n = 3,740). These estimates were used to calculate sample sizes required for a future RCT. RESULTS: Five-year risk of stroke recurrence using Kaplan-Meier estimates was 13.7 (95% CI 10.5-17.9) overall, 14.9% (95% CI 10.2-21.6) in those with high-risk PFO features. Predicted relative reduction in the event rate with PFO closure was 12.9% overall, 48.8% in those with a high-risk PFO feature. Using these estimates, enrolling all older patients with cryptogenic stroke and PFO would require much larger samples than those used for prior PFO closure trials, but selectively enrolling patients with high-risk PFO features would require totals of 630 patients for 90% power and 471 patients for 80% power, with an average of 5 years of follow-up. DISCUSSION: Based on our projections, anticipated effect sizes in older patients with high-risk features make a trial in these subjects feasible. With lengthening life expectancy in almost all regions of the world, the utility of PFO closure in older adults is increasingly important to explore.

Optimization of Benzoxazinorifamycins to Minimize hPXR Activation for the Treatment of Tuberculosis and HIV Coinfection.

Tuberculosis (TB) is one of the most significant world health problems, responsible for 1.5 M deaths in 2020, and yet, current treatments rely largely on 40 year old paradigms. Although the rifamycins (RIFs), best exemplified by the drug rifampin (RMP), represent a well-studied and therapeutically effective chemotype that targets the bacterial RNA polymerase (RNAP), these agents still suffer from serious drawbacks including the following: 3-9 month treatment times; cytochrome P450 (Cyp450) induction [particularly problematic for human immunodeficiency virus-Mycobacterium tuberculosis (MTB) co-infection]; and the existence of RIF-resistant (RIFR) MTB strains. We have utilized a structure-based drug design approach to synthesize and test 15 benzoxazinorifamycins (bxRIFs), congeners of the clinical candidate rifalazil, to minimize human pregnane X receptor (hPXR) activation while improving potency against MTB. We have determined the compounds' activation of the hPXR [responsible for inducing Cyp450 3A4 (CYP3A4)]. Compound IC50s have been determined against the wild-type and the most prevalent RIFR (ß-S450L) mutant MTB RNAPs. We have also determined their bactericidal activity against "normal" replicating MTB and a model for non-replicating, persister MTB. We have identified a minimal substitution and have probed larger substitutions that exhibit negligible hPXR activation (1.2-fold over the dimethyl sulfoxide control), many of which are 5- to 10-fold more potent against RNAPs and MTB than RMP. Importantly, we have analogues that are essentially equipotent against replicating MTB and non-replicating persister MTB, a property that is correlated with faster kill rates and may lead to shorter treatment durations. This work provides a proof of principle that the ansamycin core remains an attractive and effective scaffold for novel and dramatically improved RIFs.

Optimization of Benzoxazinorifamycins to Improve Mycobacterium tuberculosis RNA Polymerase Inhibition and Treatment of Tuberculosis.

Rifampin (RMP), a very potent inhibitor of the Mycobacterium tuberculosis (MTB) RNA polymerase (RNAP), remains a keystone in the treatment of tuberculosis since its introduction in 1965. However, rifamycins suffer from serious drawbacks, including 3- to 9-month treatment times, Cyp450 induction (particularly problematic for HIV-MTB coinfection), and resistant mutations within RNAP that yield RIF-resistant (RIFR) MTB strains. There is a clear and pressing need for improved TB therapies. We have utilized a structure-based drug design approach to synthesize and test novel benzoxazinorifamycins (bxRIF), congeners of the clinical candidate rifalazil. Our goal is to gain binding interactions that will compensate for the loss of RIF-binding affinity to the (RIFR) MTB RNAP and couple those with substitutions that we have previously found that essentially eliminate Cyp450 induction. Herein, we report a systematic exploration of 42 substituted bxRIFs that have yielded an analogue (27a) that has an excellent in vitro activity (MTB RNAP inhibition, MIC, MBC), enhanced (∼30-fold > RMP) activity against RIFR MTB RNAP, negligible hPXR activation, good mouse pharmacokinetics, and excellent activity with no observable adverse effects in an acute mouse TB model. In a time-kill study, 27a has a 7 day MBC that is ∼10-fold more potent than RMP. These results suggest that 27a may exhibit a faster kill rate than RMP, which could possibly reduce the clinical treatment time. Our synthetic protocol enabled the synthesis of ∼2 g of 27a at >95% purity in 3 months, demonstrating the feasibility of scale-up synthesis of bxRIFs for preclinical and clinical studies.

Identification of Pax protein inhibitors that suppress target gene expression and cancer cell proliferation.

The Pax family of developmental control genes are frequently deregulated in human disease. In the kidney, Pax2 is expressed in developing nephrons but not in adult proximal and distal tubules, whereas polycystic kidney epithelia or renal cell carcinoma continues to express high levels. Pax2 reduction in mice or cell culture can slow proliferation of cystic epithelial cells or renal cancer cells. Thus, inhibition of Pax activity may be a viable, cell-type-specific therapy. We designed an unbiased, cell-based, high-throughput screen that identified triazolo pyrimidine derivatives that attenuate Pax transactivation ability. We show that BG-1 inhibits Pax2-positive cancer cell growth and target gene expression but has little effect on Pax2-negative cells. Chromatin immunoprecipitation suggests that these inhibitors prevent Pax protein interactions with the histone H3K4 methylation complex at Pax target genes in renal cells. Thus, these compounds may provide structural scaffolds for kidney-specific inhibitors with therapeutic potential.

Synthesis and biological activity of conformationally restricted indole-based inhibitors of neurotropic alphavirus replication: Generation of a three-dimensional pharmacophore.

We have previously reported the development of indole-based CNS-active antivirals for the treatment of neurotropic alphavirus infection, but further optimization is impeded by a lack of knowledge of the molecular target and binding site. Herein we describe the design, synthesis and evaluation of a series of conformationally restricted analogues with the dual objectives of improving potency/selectivity and identifying the most bioactive conformation. Although this campaign was only modestly successful at improving potency, the sharply defined SAR of the rigid analogs enabled the definition of a three-dimensional pharmacophore, which we believe will be of value in further analog design and virtual screening for alternative antiviral leads.

High-throughput screens for agonists of bone morphogenetic protein (BMP) signaling identify potent benzoxazole compounds.

Bone morphogenetic protein (BMP) signaling is critical in renal development and disease. In animal models of chronic kidney disease (CKD), re-activation of BMP signaling is reported to be protective by promoting renal repair and regeneration. Clinical use of recombinant BMPs, however, requires harmful doses to achieve efficacy and is costly because of BMPs' complex synthesis. Therefore, alternative strategies are needed to harness the beneficial effects of BMP signaling in CKD. Key aspects of the BMP signaling pathway can be regulated by both extracellular and intracellular molecules. In particular, secreted proteins like noggin and chordin inhibit BMP activity, whereas kielin/chordin-like proteins (KCP) enhance it and attenuate kidney fibrosis or CKD. Clinical development of KCP, however, is precluded by its size and complexity. Therefore, we propose an alternative strategy to enhance BMP signaling by using small molecules, which are simpler to synthesize and more cost-effective. To address our objective, here we developed a small-molecule high-throughput screen (HTS) with human renal cells having an integrated luciferase construct highly responsive to BMPs. We demonstrate the activity of a potent benzoxazole compound, sb4, that rapidly stimulated BMP signaling in these cells. Activation of BMP signaling by sb4 increased the phosphorylation of key second messengers (SMAD-1/5/9) and also increased expression of direct target genes (inhibitors of DNA binding, Id1 and Id3) in canonical BMP signaling. Our results underscore the feasibility of utilizing HTS to identify compounds that mimic key downstream events of BMP signaling in renal cells and have yielded a lead BMP agonist.

Development of an in silico model for human skin permeation based on a Franz cell skin permeability assay.

A multiple linear regression QSAR model was developed based on a set of 61 compounds with internally consistent permeability data measured across Franz cell. The data was normalized using a mean permeability value of a reference compound, 3-isobutyl-1-methylxanthine (IBMX). The QSAR model contained only five simple descriptors and had a correlation coefficient, r(2) of 0.77 between experimental and calculated values for skin permeability. The mean absolute error (MAE) was 0.3 for the entire set and the cross validation coefficient, q(2) was 0.71. The in silico skin permeability model was used as a filter for virtual libraries and to optimize skin permeation of specific compounds for several dermatology discovery projects.

The development and validation of a computational model to predict rat liver microsomal clearance.

As the cost of discovering and developing new pharmaceutically relevant compounds continues to rise, it is increasingly important to select the right molecules to prosecute very early in drug discovery. The development of high throughput in vitro assays of hepatic metabolic clearance has allowed for vast quantities of data generation; however, these large screens are still costly and remain dependant on animal usage. To further expand the value of these screens and ultimately aid in animal usage reduction, we have developed an in silico model of rat liver microsomal (RLM) clearance. This model combines a large amount of rat clearance data (n = 27,697) generated at multiple Pfizer laboratories to represent the broadest possible chemistry space. The model predicts RLM stability (with 82% accuracy and a kappa value of 0.65 for test data set) based solely on chemical structural inputs, and provides a clear assessment of confidence in the prediction. The current in silico model should help accelerate the drug discovery process by using confidence-based stability-driven prioritization, and reduce cost by filtering out the most unstable/undesirable molecules. The model can also increase efficiency in the evaluation of chemical series by optimizing iterative testing and promoting rational drug design.

In silico prediction of ionization constants of drugs.

Most pharmacologically active molecules contain one or more ionizing groups, and it is well-known that knowledge of the ionization state of a drug, indicated by the pKa value, is critical for understanding many properties important to the drug discovery and development process. The ionization state of a compound directly influences such important pharmaceutical characteristics as aqueous solubility, permeability, crystal structure, etc. Tremendous advances have been made in the field of experimental determination of pKa, in terms of both quantity/speed and quality/accuracy. However, there still remains a need for accurate in silico predictions of pKa both to estimate this parameter for virtual compounds and to focus screening efforts of real compounds. The computer program SPARC (SPARC Performs Automated Reasoning in Chemistry) was used to predict the ionization state of a drug. This program has been developed based on the solid physical chemistry of reactivity models and applied to successfully predict numerous physical properties as well as chemical reactivity parameters. SPARC predicts both macroscopic and microscopic pKa values strictly from molecular structure. In this paper, we describe the details of the SPARC reactivity computational methods and its performance on predicting the pKa values of known drugs as well as Pfizer internal discovery/development compounds. A high correlation (r2=0.92) between experimental and the SPARC calculated pKa values was obtained with root-mean-square error (RMSE) of 0.78 log unit for a set of 123 compounds including many known drugs. For a set of 537 compounds from the Pfizer internal dataset, correlation coefficient r2=0.80 and RMSE=1.05 were obtained.

On-the-fly selection of a training set for aqueous solubility prediction.

Training sets are usually chosen so that they represent the database as a whole; random selection helps to maintain this integrity. In this study, the prediction of aqueous solubility was used as a specific example of using the individual molecule for which solubility is desired, the target molecule, as the basis for choosing a training set. Similarity of the training set to the target molecule rather than a random allocation was used as the selection criteria. The Tanimoto coefficients derived from Daylight's binary fingerprints were used as the molecular similarity selection tool. Prediction models derived from this type of customization will be designated as "on-the-fly local" models because a new model is generated for each target molecule which is necessarily local. Such models will be compared with "global" models which are derived from a one-time "preprocessed" partitioning of training and test sets which use fixed fitted parameters for each target molecule prediction. Although both fragment and molecular descriptors were examined, a minimum set of MOE (molecular operating environment) molecular descriptors were found to be more efficient and were use for both on-the-fly local and preprocessed global models. It was found that on-the-fly local predictions were more accurate (r2=0.87) than the preprocessed global predictions (r2=0.74) for the same test set. In addition, their precision was shown to increase as the degree of similarity increases. Correlation and distribution plots were used to visualize similarity cutoff groupings and their chemical structures. In summary, rapid "on-the-fly" similarity selection can enable the customization of a training set to each target molecule for which solubility is desired. In addition, the similarity information and the model's fitting statistics give the user criteria to judge the validity of the prediction since it is always possible that good prediction cannot be obtained because the database and the target molecule are too dissimilar. Although the rapid processing speed of binary fingerprints enable the "on-the-fly" real time prediction, slower but more feature rich similarity measures may improve follow-up predictions.

Development of in silico models for human liver microsomal stability.

We developed highly predictive classification models for human liver microsomal (HLM) stability using the apparent intrinsic clearance (CL(int, app)) as the end point. HLM stability has been shown to be an important factor related to the metabolic clearance of a compound. Robust in silico models that predict metabolic clearance are very useful in early drug discovery stages to optimize the compound structure and to select promising leads to avoid costly drug development failures in later stages. Using Random Forest and Bayesian classification methods with MOE, E-state descriptors, ADME Keys, and ECFP_6 fingerprints, various highly predictive models were developed. The best performance of the models shows 80 and 75% prediction accuracy for the test and validation sets, respectively. A detailed analysis of results will be shown, including an assessment of the prediction confidence, the significant descriptors, and the application of these models to drug discovery projects.

Restricted conformation analogues of an anthelmintic cyclodepsipeptide.

Six analogues of the anthelmintic cyclodepsipeptide PF1022A were prepared, each containing a small ring fused to the macrocycle to restrict the number of conformations the larger ring can adopt. It was anticipated that such conformational changes could lead to enhanced biological activity and selectivity. The analogues form two series of three members each. In one series, a carbon-based molecular bridge joins the methyl of a leucine residue with the methyl of its closest lactic acid residue to form five-, six-, and seven-membered lactam rings. In the second series, a leucine residue is replaced with five-, six-, and seven-membered nitrogen heterocycles. Decreasing the size of the small ring in the lactam series increasingly distorts the macrocycle and consistently decreases activity relative to PF1022A. In the leucine series, a similar trend is observed. Molecular modeling of PF1022A along with the analogues described herein suggests that the ability to exist in a highly symmetrical conformational state is a necessary condition for biological activity.