Angiotensin receptor blockade and neprilysin (NEP) inhibition together offer potential benefits for the treatment of hypertension and heart failure. LCZ696 is a novel single molecule comprising molecular moieties of valsartan and NEP inhibitor prodrug AHU377 (1:1 ratio). Oral administration of LCZ696 caused dose-dependent increases in atrial natriuretic peptide immunoreactivity (due to NEP inhibition) in Sprague-Dawley rats and provided sustained, dose-dependent blood pressure reductions in hypertensive double-transgenic rats. In healthy participants, a randomized, double-blind, placebo-controlled study (n = 80) of single-dose (200-1200 mg) and multiple-dose (50-900 mg once daily for 14 days) oral administration of LCZ696 showed that peak plasma concentrations were reached rapidly for valsartan (1.6-4.9 hours), AHU377 (0.5-1.1 hours), and its active moiety, LBQ657 (1.8-3.5 hours). LCZ696 treatment was associated with increases in plasma cGMP, renin concentration and activity, and angiotensin II, providing evidence for NEP inhibition and angiotensin receptor blockade. In a randomized, open-label crossover study in healthy participants (n = 56), oral LCZ696 400 mg and valsartan 320 mg were shown to provide similar exposure to valsartan (geometric mean ratio [90% confidence interval]: AUC(0-infinity) 0.90 [0.82-0.99]). LCZ696 was safe and well tolerated. These data support further clinical development of LCZ696, a novel, orally bioavailable, dual-acting angiotensin receptor-NEP inhibitor (ARNi) for hypertension and heart failure.

Reproducibly differential responses to different classes of antihypertensive agents are observed among hypertensive patients and may be due to interindividual differences in hypertension pathology. Computational models provide a tool for investigating the impact of underlying disease mechanisms on the response to antihypertensive therapies with different mechanisms of action. We present the development, calibration, validation, and application of an extension of the Guyton/Karaaslan model of blood pressure regulation. The model incorporates a detailed submodel of the renin-angiotensin-aldosterone system (RAAS), allowing therapies that target different parts of this pathway to be distinguished. Literature data on RAAS biomarker and blood pressure responses to different classes of therapies were used to refine the physiological actions of ANG II and aldosterone on renin secretion, renal vascular resistance, and sodium reabsorption. The calibrated model was able to accurately reproduce the RAAS biomarker and blood pressure responses to combinations of dual-RAAS agents, as well as RAAS therapies in combination with diuretics or calcium channel blockers. The final model was used to explore the impact of underlying mechanisms of hypertension on the blood pressure response to different classes of antihypertensive agents. Simulations indicate that the underlying etiology of hypertension can impact the magnitude of response to a given class of therapy, making a patient more sensitive to one class and less sensitive others. Given that hypertension is usually the result of multiple mechanisms, rather than a single factor, these findings yield insight into why combination therapy is often required to adequately control blood pressure.

In standard risk assessment methods for carcinogenic or noncarcinogenic chemicals, quantitative methods for evaluating interindividual variability are not explicitly considered. These differences are currently considered by the use of statistical confidence limits or default uncertainty factors. This investigation consisted of multiple tasks aimed at making quantitative predictions of interindividual differences in susceptibility by using physiologically based pharmacokinetic (PBPK) models. Initially, a systematic, comprehensive review of the literature was conducted to identify any quantitative information related to gender- or age-specific physiological and biochemical factors that could influence susceptibility to chemical exposure. These data were then organized from a pharmacokinetic perspective by process and by chemical class to identify key factors likely to have a significant impact on susceptibility as it relates to internal target tissue dose. Overall, a large number of age- and gender-specific quantitative differences in pharmacokinetic parameters were identified. The majority of these differences were identified between neonates/children and adults, with fewer differences identified between young adults and the elderly. The next phase of this work consists of using PBPK models to develop examples of approaches through the development of case studies. The goal of the case studies is to continue to develop a methodology that incorporates PBPK modeling to assess the likelihood that a chemical or class of chemicals may present an age- or gender-specific risk. The case studies should also demonstrate practical methods for quantitatively incorporating information on age- and gender-specific pharmacokinetic differences in risk assessments for chemicals.

The drug development process is divided into phases with decisions required on compound selection and promotion to each subsequent development phase. In preclinical drug development the main objective is to bring the compound into human trials and there is an inability of many preclinical information packages to predict clinical responses. Since clinical responses are functions of the dose, the human dose anticipation should be a key deliverable of any preclinical package of drug candidate. The human dose should be anticipated by integration of information from multiple sources, in vitro and in vivo, non-human and human, using a variety of methodologies and approaches. Prediction of human safe and active dose relies on the availability of validated animal models for effect. Although there are many exceptions to the rule, the paper defines a four-step approach for the anticipation of human dose for first-in-man trials: 1, characterization of non-human exposure-response relationships; 2, correction for interspecies differences; 3, diagnosing compound absorption, distribution, metabolism and excretion (ADME) properties and prediction of human pharmacokinetics; and 4, prediction of human dose-responses and dose selection for phase I protocols.