Direct costs of first-generation protease inhibitors for the treatment of genotype 1 chronic hepatitis C viral infection

To evaluate the cost-effectiveness of Hepatitis C therapy, robust real-world data are needed to understand the costs and benefits of treatment alternatives. The objective of this study was to evaluate the true direct cost of treatment in an unselected sequential population of patients treated at a tertiary care centre for hepatitis C virus genotype 1. A total of 200 consecutive patients were treated with interferon, ribavirin and a first-generation direct-acting antiviral agent (DAA) between 2011 and 2013. A total of 41% had cirrhosis, 31% were prior relapsers, and 41% were prior partial or null responders. Costs used were wholesale acquisition cost prices for medications, average hospital costs per day for each diagnosis code based on US inpatient hospital charges. All costs were adjusted to 2013 dollars. Sustained virologic response (SVR) was achieved in 97 patients (48.5%). A total of 14% experienced relapse, 19% breakthrough or nonresponse, and 18.5% discontinued secondary to side effects. Twenty per cent of patients had at least one hospitalization attributable to a complication of therapy. Thirty-seven per cent of patients required erythropoietin-stimulating agents, 16% received filgastrim, and 15% needed a red blood cell transfusion. The mean overall cost of treatment was $83,851 per patient. The cost per SVR was $172,889; $266,670 for patients with cirrhosis. The costs per SVR after treatment with first-generation DAAs are dependent on the stage of disease and therapy side effects. These real-world costs significantly exceed those described in prior cost-effectiveness assessments and should be used instead for future studies.

Nonequilibrium Solvent Effects during Photodissociation in Liquids: Dynamical Energy Surfaces, Caging, and Chemical Identity

In the gas phase, potential energy surfaces can be used to provide insight into the details of photochemical reaction dynamics. In solution, however, it is unclear what potential energy surfaces, if any, can be used to describe even simple chemical reactions such as the photodissociation of a diatomic solute. In this paper, we use mixed quantum/classical (MQC) molecular dynamics (MD) to study the photodissociation of Na₂⁺ in both liquid Ar and liquid tetrahydrofuran (THF). We examine both the gas-phase potential surfaces and potentials of mean force (PMF), which assume that the solvent remains at equilibrium with the solute throughout the photodissociation process and show that neither resemble a nonequilibrium dynamical energy surface that is generated by taking the time integral of work. For the photodissociation of Na₂⁺ in liquid Ar, the dynamical energy surface shows clear signatures of solvent caging, and the degree of caging is directly related to the mass of the solvent atoms. For Na₂⁺ in liquid THF, local specific interactions between the solute and solvent lead to changes in chemical identity that create a kinetic trap that effectively prevents the molecule from dissociating. The results show that nonequilibrium effects play an important role even in simple solution-phase reactions, requiring the use of dynamical energy surface to understand such chemical events.

Bond-Breaking Reactions Encounter Distinct Solvent Environments Causing Breakdown of Linear Response

Solvent effects are important for understanding solution-phase chemical reactions. Surprisingly, very few studies have explored how solvent dynamics change during the course of a reaction with solutes that encounter a wide range of configurations. Here, we use quantum simulation methods to explore the solvent dynamics during a solution-phase bond-breaking reaction: the photodissociation of Na₂⁺ in liquid Ar. We find that the solute experiences a small number of distinct solvent environments that change in a discrete fashion as the bond lengthens. In characterizing the solvent environments, we show also that linear response fails by all measures, even when nonstationarity of solvent dynamics is considered. This observation of distinct solvent response environments with a solvent that can undergo only translational motions highlights the complexity of solute-solvent interactions, but that there are only a few environments gives hope to the idea that solvation dynamics can be understood for solution-phase reactions that explore a wide configuration space.

Solvent Control of Chemical Identity Can Change Photodissociation into Photoisomerization

In solution-phase chemistry, the solvent is often considered to be merely a medium that allows reacting solutes to encounter each other. In this work, however, we show that moderate locally specific solute-solvent interactions can affect not only the nature of the solute but also the types of reactive chemistry. We use quantum simulation methods to explore how solvent participation in solute chemical identity alters reactions involving the breaking of chemical bonds. In particular, we explore the photoexcitation dynamics of Na₂⁺ dissolved in liquid tetrahydrofuran. In the gas phase, excitation of Na₂⁺ directly leads to dissociation, but in solution, photoexcitation leads to an isomerization reaction involving rearrangement of the first-shell solvent molecules; this isomerization must go to completion before the solute can dissociate. Despite the complexity, the solution-phase reaction dynamics can be captured by a two-dimensional energy surface where one dimension involves only the isomerization of the first-shell solvent molecules.

Using Machine Learning to Understand the Causes of Quantum Decoherence in Solution-Phase Bond-Breaking Reactions

Decoherence is a fundamental phenomenon that occurs when an entangled quantum state interacts with its environment, leading to collapse of the wave function. The inevitability of decoherence provides one of the most intrinsic limits of quantum computing. However, there has been little study of the precise chemical motions from the environment that cause decoherence. Here, we use quantum molecular dynamics simulations to explore the photodissociation of Na2+ in liquid Ar, in which solvent fluctuations induce decoherence and thus determine the products of chemical bond breaking. We use machine learning to characterize the solute-solvent environment as a high-dimensional feature space that allows us to predict when and onto which photofragment the bonding electron will localize. We find that reaching a requisite photofragment separation and experiencing out-of-phase solvent collisions underlie decoherence during chemical bond breaking. Our work highlights the utility of machine learning for interpreting complex solution-phase chemical processes as well as identifies the molecular underpinnings of decoherence.