Emerging Therapeutics, Technologies, and Drug Development Strategies to Address Patient Nonadherence and Improve Tuberculosis Treatment.

Imperfect medication adherence remains the biggest predictor of treatment failure for patients with tuberculosis. Missed doses during treatment lead to relapse, tuberculosis resistance, and further spread of disease. Understanding individual patient phenotypes, population pharmacokinetics, resistance development, drug distribution to tuberculosis lesions, and pharmacodynamics at the site of infection is necessary to fully measure the impact of adherence on patient outcomes. To decrease the impact of expected variabilityin drug intake on tuberculosis outcomes, an improvement in patient adherence and new forgiving regimens that protect against missed doses are needed. In this review, we summarize emerging technologies to improve medication adherence in clinical practice and provide suggestions on how digital adherence technologies can be incorporated in clinical trials and practice and the drug development pipeline that will lead to more forgiving regimens and benefit patients suffering from tuberculosis.

Modeling the potential impact of emerging innovations on achievement of Sustainable Development Goals related to maternal, newborn, and child health.

BACKGROUND: Innovations that improve the affordability, accessibility, or effectiveness of health care played a major role in the Millennium Development Goal achievements and will be critical for reaching the ambitious new Sustainable Development Goal (SDG) health targets. Mechanisms to identify and prioritize innovations are essential to inform future investment decisions. METHODS: Innovation Countdown 2030 crowdsourced health innovations from around the world and engaged recognized experts to systematically assess their lifesaving potential by 2030. A health impact modeling approach was developed and used to quantify the costs and lives saved for select innovations identified as having great promise for improving maternal, newborn, and child health. RESULTS: Preventive innovations targeting health conditions with a high mortality burden had the greatest impact in regard to the absolute number of estimated lives saved. The largest projected health impact was for a new tool for small-scale water treatment that automatically chlorinates water to a safe concentration without using electricity or moving parts. An estimated 1.5 million deaths from diarrheal disease among children under five could be prevented by 2030 by scaling up use of this technology. Use of chlorhexidine for umbilical cord care was associated with the second highest number of lives saved. CONCLUSIONS: The results show why a systematic modeling approach that can compare and contrast investment opportunities is important for prioritizing global health innovations. Rigorous impact estimates are needed to allocate limited resources toward the innovations with great potential to advance the SDGs.

Modeling the public health impact of malaria vaccines for developers and policymakers.

BACKGROUND: Efforts to develop malaria vaccines show promise. Mathematical model-based estimates of the potential demand, public health impact, and cost and financing requirements can be used to inform investment and adoption decisions by vaccine developers and policymakers on the use of malaria vaccines as complements to existing interventions. However, the complexity of such models may make their outputs inaccessible to non-modeling specialists. This paper describes a Malaria Vaccine Model (MVM) developed to address the specific needs of developers and policymakers, who need to access sophisticated modeling results and to test various scenarios in a user-friendly interface. The model's functionality is demonstrated through a hypothetical vaccine. METHODS: The MVM has three modules: supply and demand forecast; public health impact; and implementation cost and financing requirements. These modules include pre-entered reference data and also allow for user-defined inputs. The model includes an integrated sensitivity analysis function. Model functionality was demonstrated by estimating the public health impact of a hypothetical pre-erythrocytic malaria vaccine with 85% efficacy against uncomplicated disease and a vaccine efficacy decay rate of four years, based on internationally-established targets. Demand for this hypothetical vaccine was estimated based on historical vaccine implementation rates for routine infant immunization in 40 African countries over a 10-year period. Assumed purchase price was $5 per dose and injection equipment and delivery costs were $0.40 per dose. RESULTS: The model projects the number of doses needed, uncomplicated and severe cases averted, deaths and disability-adjusted life years (DALYs) averted, and cost to avert each. In the demonstration scenario, based on a projected demand of 532 million doses, the MVM estimated that 150 million uncomplicated cases of malaria and 1.1 million deaths would be averted over 10 years. This is equivalent to 943 uncomplicated cases and 7 deaths averted per 1,000 vaccinees. In discounted 2011 US dollars, this represents $11 per uncomplicated case averted and $1,482 per death averted. If vaccine efficacy were reduced to 75%, the estimated uncomplicated cases and deaths averted over 10 years would decrease by 14% and 19%, respectively. CONCLUSIONS: The MVM can provide valuable information to assist decision-making by vaccine developers and policymakers, information which will be refined and strengthened as field studies progress allowing further validation of modeling assumptions.

Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data.

Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials.

The MANDATE model for evaluating interventions to reduce postpartum hemorrhage.

OBJECTIVE: To create a comprehensive model of the comparative impact of various interventions on maternal, fetal, and neonatal (MFN) mortality. METHODS: The major conditions and sub-conditions contributing to MFN mortality in low-resource areas were identified, and the prevalence and case fatality rates documented. Available interventions were mapped to these conditions, and intervention coverage and efficacy were identified. Finally, a computer model developed by the Maternal and Neonatal Directed Assessment of Technology (MANDATE) initiative estimated the potential of current and new interventions to reduce mortality. RESULTS: For PPH, the sub-causes, prevalence, and MFN case fatality rates were calculated. Available interventions were mapped to these sub-causes. Most available interventions did not prevent or treat the overall condition of PPH, but rather sub-conditions associated with hemorrhage and thus prevented only a fraction of the associated deaths. CONCLUSION: The majority of current interventions address sub-conditions that cause death, rather than the overall condition; thus, the potential number of lives saved is likely to be overestimated. Additionally, the location at which mother and infant receive care affects intervention effectiveness and, therefore, the potential to save lives. A comprehensive view of MFN conditions is needed to understand the impact of any potential intervention.