Long-Term Costs and Health Impact of Continued Global Fund Support for Antiretroviral Therapy

Background: By the end of 2011 Global Fund investments will be supporting 3.5 million people on antiretroviral therapy (ART) in 104 low- and middle-income countries. We estimated the cost and health impact of continuing treatment for these patients through 2020. Methods and Findings: Survival on first-line and second-line ART regimens is estimated based on annual retention rates reported by national AIDS programs. Costs per patient-year were calculated from country-reported ARV procurement prices, and expenditures on laboratory tests, health care utilization and end-of-life care from in-depth costing studies. Of the 3.5 million ART patients in 2011, 2.3 million will still need treatment in 2020. The annual cost of maintaining ART falls from $1.9 billion in 2011 to$1.7 billion in 2020, as a result of a declining number of surviving patients partially offset by increasing costs as more patients migrate to second-line therapy. The Global Fund is expected to continue being a major contributor to meeting this financial need, alongside other international funders and domestic resources. Costs would be $150 million less in 2020 with an annual 5$150-370 million less with a 5%-12% annual decline in second-line prices, but $200 million higher in 2020 with phase out of stavudine (d4T), or$200 million higher with increased migration to second-line regimens expected if all countries routinely adopted viral load monitoring. Deaths postponed by ART correspond to 830,000 life-years saved in 2011, increasing to around 2.3 million life-years every year between 2015 and 2020. Conclusions: Annual patient-level direct costs of supporting a patient cohort remain fairly stable over 2011-2020, if current antiretroviral prices and delivery costs are maintained. Second-line antiretroviral prices are a major cost driver, underscoring the importance of investing in treatment quality to improve retention on first-line regimens

Voluntary Medical Male Circumcision: Modeling the Impact and Cost of Expanding Male Circumcision for HIV Prevention in Eastern and Southern Africa

Emmanuel Njeuhmeli and colleagues estimate the impact and cost of scaling up adult medical male circumcision in 13 priority countries in eastern and southern Africa, finding that reaching 80% coverage and maintaining it until 2025 would avert 3.36 million new HIV infections

Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. © 2014 Hogg et al

Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors

Whole-exome sequencing of cell-free DNA (cfDNA) could enable comprehensive profiling of tumors from blood but the genome-wide concordance between cfDNA and tumor biopsies is uncertain. Here we report ichorCNA, software that quantifies tumor content in cfDNA from 0.1× coverage whole-genome sequencing data without prior knowledge of tumor mutations. We apply ichorCNA to 1439 blood samples from 520 patients with metastatic prostate or breast cancers. In the earliest tested sample for each patient, 34% of patients have ≥10% tumor-derived cfDNA, sufficient for standard coverage whole-exome sequencing. Using whole-exome sequencing, we validate the concordance of clonal somatic mutations (88%), copy number alterations (80%), mutational signatures, and neoantigens between cfDNA and matched tumor biopsies from 41 patients with ≥10% cfDNA tumor content. In summary, we provide methods to identify patients eligible for comprehensive cfDNA profiling, revealing its applicability to many patients, and demonstrate high concordance of cfDNA and metastatic tumor whole-exome sequencing

Space and time complexities for network-based (SSA) and network-free (NF) stochastic simulation algorithms.

a<p>No dependency graph.</p>b<p>Dependency graph <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Gibson1" target="_blank">[37]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Cao1" target="_blank">[38]</a>.</p>c<p>Logarithmic classes (with dependency graph) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Fricke1" target="_blank">[21]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Slepoy1" target="_blank">[39]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Schulze1" target="_blank">[40]</a>.</p>d<p>Next-reaction method (with dependency graph) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Gibson1" target="_blank">[37]</a>.</p>e<p>Direct method (with or without dependency graph) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Gillespie1" target="_blank">[20]</a>.</p>f<p>Polymerizing systems in gel phase <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Yang1" target="_blank">[23]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Monine1" target="_blank">[42]</a> (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi-1003544-g005" target="_blank">Fig. 5B</a>).</p>g<p>Direct method-like implementation.</p><p>Scalings are shown with respect to particle number, , and number of reactions, , or rules, . For combinatorially-complex models, . Note that time complexity is given on a “per event” (reaction/rule firing) basis. If a reaction dependency graph <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Gibson1" target="_blank">[37]</a> is used, the space and time complexities of SSA methods with respect to depend on , the maximum number of reactions updated after each reaction firing <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Gibson1" target="_blank">[37]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Cao1" target="_blank">[38]</a>. In combinatorially-complex models, often increases with (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544.s002" target="_blank">Figure S1</a> of the supporting information). The time complexity of SSA methods with respect to also depends on the method used for selecting the next reaction to fire in the system. Scalings are shown for three different SSA variants that use different selection methods <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Gillespie1" target="_blank">[20]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Fricke1" target="_blank">[21]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Gibson1" target="_blank">[37]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Slepoy1" target="_blank">[39]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Schulze1" target="_blank">[40]</a>. Also note that optimized variants of the direct method <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Fricke1" target="_blank">[21]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Cao1" target="_blank">[38]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-McCollum1" target="_blank">[41]</a> have been shown to outperform methods with lower asymptotic complexity in some cases <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Cao1" target="_blank">[38]</a>. Space and time complexities of the NF algorithm with respect to assume no dependency graph and that the next rule to fire is selected as in Gillespie's direct method <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544-Gillespie1" target="_blank">[20]</a>, although in principle other variants are possible.</p

Memory use vs. simulated volume for different simulation methods, including a hypothetical automated HPP (aHPP).

<p>For finite networks, aHPP memory use plateaus once the entire reaction network has been generated. For infinite networks, the scaling at large volumes should fall somewhere between constant and linear (no worse than HPP) depending on the model (see Sec. S2 of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi.1003544.s003" target="_blank">Text S1</a> for an analysis).</p

HPP performance analysis for the actin polymerization model.

<p>(A) peak memory usage (<i>left</i>: absolute, <i>right</i>: relative to NFsim); (B) CPU run time (<i>left</i>: absolute, <i>right</i>: relative to NFsim); (C) number of reaction events fired during a simulation (); (D) equilibrium distribution of actin polymer lengths (). The slight deviation from linearity for ‘NF’ in (A) is an artifact of how memory is allocated in NFsim.</p

Simple illustration of ambiguity in the products of reaction rules.

<p>(A) A simple rule encodes the reversible binding of two molecule types, A and B. (B)–(D) If both molecules have multiple binding sites then they may be present within arbitrarily complex complexes. Breaking the bond between A and B thus produces a variety of product species, some of which may correspond to population species and others not. Dashed line represents a bond addition/deletion operation.</p

HPP performance analyses for various lumping thresholds at cell fraction .

<p>(A) TLBR; (B) Actin; (C) ; (D) EGFR. In all plots, threshold values for different lumping sets are shown on the x-axis. For TLBR and Actin, some thresholds yield the same set of population species as larger thresholds and are thus omitted from the figures. For TLBR, results for thresholds are omitted due to impractically large partial networks in those cases. Results for NFsim (‘NF’) and the hand-picked lumping sets from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi-1003544-g005" target="_blank">Figs. 5</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003544#pcbi-1003544-g008" target="_blank">8</a> (‘HPP’) are shown in all plots for comparison. Error bars show standard error (three samples).</p