Handan He

An adenosine nucleoside inhibitor of dengue virus

Dengue virus (DENV), a mosquito-borne flavivirus, is a major public health threat. The virus poses risk to 2.5 billion people worldwide and causes 50 to 100 million human infections each year. Neither a vaccine nor an antiviral therapy is currently available for prevention and treatment of DENV infection. Here, we report a previously undescribed adenosine analog, NITD008, that potently inhibits DENV both in vitro and in vivo. In addition to the 4 serotypes of DENV, NITD008 inhibits other flaviviruses, including West Nile virus, yellow fever virus, and Powassan virus. The compound also suppresses hepatitis C virus, but it does not inhibit nonflaviviruses, such as Western equine encephalitis virus and vesicular stomatitis virus. A triphosphate form of NITD008 directly inhibits the RNA-dependent RNA polymerase activity of DENV, indicating that the compound functions as a chain terminator during viral RNA synthesis. NITD008 has good in vivo pharmacokinetic properties and is biologically available through oral administration. Treatment of DENV-infected mice with NITD008 suppressed peak viremia, reduced cytokine elevation, and completely prevented the infected mice from death. No observed adverse effect level (NOAEL) was achieved when rats were orally dosed with NITD008 at 50 mg/kg daily for 1 week. However, NOAEL could not be accomplished when rats and dogs were dosed daily for 2 weeks. Nevertheless, our results have proved the concept that a nucleoside inhibitor could be developed for potential treatment of flavivirus infections.

PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach

The objective of this study is to assess the effectiveness of physiologically based pharmacokinetic (PBPK) models for simulating human plasma concentration-time profiles for the unique drug dataset of blinded data that has been assembled as part of a Pharmaceutical Research and Manufacturers of America initiative. Combinations of absorption, distribution, and clearance models were tested with a PBPK approach that has been developed from published equations. An assessment of the quality of the model predictions was made on the basis of the shape of the plasma time courses and related parameters. Up to 69% of the simulations of plasma time courses made in human demonstrated a medium to high degree of accuracy for intravenous pharmacokinetics, whereas this number decreased to 23% after oral administration based on the selected criteria. The simulations resulted in a general underestimation of drug exposure (Cmax and AUC0- t ). The explanations for this underestimation are diverse. Therefore, in general it may be due to underprediction of absorption parameters and/or overprediction of distribution or oral first-pass. The implications of compound properties are demonstrated. The PBPK approach based on in vitro-input data was as accurate as the approach based on in vivo data. Overall, the scientific benefit of this modeling study was to obtain more extensive characterization of predictions of human PK from PBPK methods.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: Comparative assessement of prediction methods of human clearance

The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.

Absorption, Metabolism, and Excretion of [14C]Vildagliptin, a Novel Dipeptidyl Peptidase 4 Inhibitor, in Humans

The absorption, metabolism, and excretion of (1-[[3-hydroxy-1-adamantyl) amino] acetyl]-2-cyano-(S)-pyrrolidine (vildagliptin), an orally active and highly selective dipeptidyl peptidase 4 inhibitor developed for the treatment of type 2 diabetes, were evaluated in four healthy male subjects after a single p.o. 100-mg dose of [14C]vildagliptin. Serial blood and complete urine and feces were collected for 168 h postdose. Vildagliptin was rapidly absorbed, and peak plasma concentrations were attained at 1.1 h postdose. The fraction of drug absorbed was calculated to be at least 85.4%. Unchanged drug and a carboxylic acid metabolite (M20.7) were the major circulating components in plasma, accounting for 25.7% (vildagliptin) and 55% (M20.7) of total plasma radioactivity area under the curve. The terminal half-life of vildagliptin was 2.8 h. Complete recovery of the dose was achieved within 7 days, with 85.4% recovered in urine (22.6% unchanged drug) and the remainder in feces (4.54% unchanged drug). Vildagliptin was extensively metabolized via at least four pathways before excretion, with the major metabolite M20.7 resulting from cyano group hydrolysis, which is not mediated by cytochrome P450 (P450) enzymes. Minor metabolites resulted from amide bond hydrolysis (M15.3), glucuronidation (M20.2), or oxidation on the pyrrolidine moiety of vildagliptin (M20.9 and M21.6). The diverse metabolic pathways combined with a lack of significant P450 metabolism (1.6% of the dose) make vildagliptin less susceptible to potential pharmacokinetic interactions with comedications of P450 inhibitors/inducers. Furthermore, as vildagliptin is not a P450 inhibitor, it is unlikely that vildagliptin would affect the metabolic clearance of comedications metabolized by P450 enzymes.

The Identification of Indacaterol as an Ultralong-Acting Inhaled β2-Adrenoceptor Agonist

Following a lipophilicity-based hypothesis, an 8-hydroxyquinolinone 2-aminoindan derived series of beta(2)-adrenoceptor agonists have been prepared and evaluated for their potential as inhaled ultralong-acting bronchodilators. Determination of their activities at the human beta(2)-adrenoceptor receptor showed symmetrical substitution of the 2-aminoindan moiety at the 5- and 6-positions delivered the targeted intermediate potency and intrinsic-efficacy profiles relative to a series of clinical reference beta(2)-adrenoceptor agonists. Further assessment with an in vitro superfused electrically stimulated guinea-pig tracheal-strip assay established the onset and duration of action time courses, which could be rationalized by considering the lipophilicity, potency, and intrinsic efficacy of the compounds. From these studies the 5,6-diethylindan analogue indacaterol 1c was shown to possess a unique profile of combining a rapid onset of action with a long duration of action. Further in vivo profiling of 1c supported the long duration of action and a wide therapeutic index following administration to the lung, which led to the compound being selected as a development candidate.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: Comparative assessment of prediction methods of human volume of distribution

The objective of this study was to evaluate the performance of various empirical, semimechanistic and mechanistic methodologies with and without protein binding corrections for the prediction of human volume of distribution at steady state (Vss ). PhRMA member companies contributed a set of blinded data from preclinical and clinical studies, and 18 drugs with intravenous clinical pharmacokinetics (PK) data were available for the analysis. In vivo and in vitro preclinical data were used to predict Vss by 24 different methods. Various statistical and outlier techniques were employed to assess the predictability of each method. There was not simply one method that predicts Vss accurately for all compounds. Across methods, the maximum success rate in predicting human Vss was 100%, 94%, and 78% of the compounds with predictions falling within tenfold, threefold, and twofold error, respectively, of the observed Vss . Generally, the methods that made use of in vivo preclinical data were more predictive than those methods that relied solely on in vitro data. However, for many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. It is recommended to initially use the in vitro tissue composition-based equations to predict Vss in preclinical species and humans, putting the assumptions and compound properties into context. As in vivo data become available, these predictions should be reassessed and rationalized to indicate the level of confidence (uncertainty) in the human Vss prediction. The top three methods that perform strongly at integrating in vivo data in this way were the Øie-Tozer, the rat -dog-human proportionality equation, and the lumped-PBPK approach. Overall, the scientific benefit of this study was to obtain greater characterization of predictions of human Vss from several methods available in the literature.

Inhibition of Dengue Virus RNA Synthesis by an Adenosine Nucleoside

ABSTRACT We recently reported that (2R,3R,4R,5R)-2-(4-amino-pyrrolo[2,3-d]pyrimidin-7-yl)-3-ethynyl-5-hydroxy-methyl-tetrahydro-furan-3,4-diol is a potent inhibitor of dengue virus (DENV), with 50% effective concentration (EC50) and cytotoxic concentration (CC50) values of 0.7 μM and >100 μM, respectively. Here we describe the synthesis, structure-activity relationship, and antiviral characterization of the inhibitor. In an AG129 mouse model, a single-dose treatment of DENV-infected mice with the compound suppressed peak viremia and completely prevented death. Mode-of-action analysis using a DENV replicon indicated that the compound blocks viral RNA synthesis. Recombinant adenosine kinase could convert the compound to a monophosphate form. Suppression of host adenosine kinase, using a specific inhibitor (iodotubercidin) or small interfering RNA (siRNA), abolished or reduced the compounds antiviral activity in cell culture. Studies of rats showed that 14C-labeled compound was converted to mono-, di-, and triphosphate metabolites in vivo. Collectively, the results suggest that this adenosine inhibitor is phosphorylated to an active (triphosphate) form which functions as a chain terminator for viral RNA synthesis.

PHRMA CPCDC Initiative on Predictive Models of Human Pharmacokinetics, Part 4: Prediction of Plasma Concentration-Time Profiles in Human from In Vivo Preclinical Data by Using the Wajima Approach

The objective of this study was to evaluate the performance of the Wajima allometry (Css -MRT) approach published in the literature, which is used to predict the human plasma concentration-time profiles from a scaling of preclinical species data. A diverse and blinded dataset of 108 compounds from PhRMA member companies was used in this evaluation. The human intravenous (i.v.) and oral (p.o.) pharmacokinetics (PK) data were available for 18 and 107 drugs, respectively. Three different scenarios were adopted for prediction of human PK profiles. In the first scenario, human clearance (CL) and steady-state volume of distribution (Vss ) were predicted by unbound fraction corrected intercept method (FCIM) and Øie-Tozer (OT) approaches, respectively. Quantitative structure activity relationship (QSAR)-based approaches (TSrat-dog ) based on compound descriptors together with rat and dog data were utilized in the second scenario. Finally, in the third scenario, CL and Vss were predicted using the FCIM and Jansson approaches, respectively. For the prediction of oral pharmacokinetics, the human bioavailability and absorption rate constant were assumed as the average of preclinical species. Various statistical techniques were used for assessing the accuracy of the simulation scenarios. The human CL and Vss were predicted within a threefold error range for about 75% of the i.v. drugs. However, the accuracy in predicting key p.o. PK parameters appeared to be lower with only 58% of simulations falling within threefold of observed parameters. The overall ability of the Css -MRT approach to predict the curve shape of the profile was in general poor and ranged between low to medium level of confidence for most of the predictions based on the selected criteria.

Case Studies for Practical Food Effect Assessments across BCS/BDDCS Class Compounds using In Silico, In Vitro, and Preclinical In Vivo Data

Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration–time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1–4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound’s BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions.

PhRMA CPCDC Initiative on Predictive Models of Human Pharmacokinetics, Part 1: Goals, Properties of the Phrma Dataset, and Comparison with Literature Datasets

This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration-time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous (n = 19) and oral pharmacokinetic data (n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts.