Measuring windows of selection for anti-malarial drug treatments.

BACKGROUND The long half-lives of malaria 'partner' drugs are a potent force selecting for drug resistance. Clinical trials can quantify this effect by estimating a window of selection (WoS), defined as the amount of time post-treatment when drug levels are sufficiently high that resistant parasites can re-establish an infection while preventing drug-sensitive parasites from establishing viable infections. METHODS The ability of clinical data to accurately estimate the true WoS was investigated using standard pharmacokinetic-pharmacodynamic models for three widely used malaria drugs: artemether-lumefantrine (AR-LF), artesunate-mefloquine (AS-MQ) and dihydroartemisinin-piperaquine (DHA-PPQ). Estimates of the clinical WoS either (1) ignored all new infections occurring after the 63-day follow-up period, as is currently done in clinical trials, or, (2) recognized that all individuals would eventually be re-infected and arbitrarily assigned them a new infection day. RESULTS The results suggest current methods of estimating the clinical WoS underestimate the true WoS by as much as 9 days for AR-LF, 33 days for AS-MQ and 7 days for DHA-PPQ. The new method of estimating clinical WoS (i.e., retaining all individuals in the analysis) was significantly better at estimating the true WoS for AR-LF and AS-MQ. CONCLUSIONS Previous studies, based on clinically observed WoS, have probably underestimated the 'true' WoS and hence the role of drugs with long half-lives in driving resistance. This has important policy implications: high levels of drug use are inevitable in mass drug administration programmes and intermittent preventative treatment programmes and the analysis herein suggests these policies will be far more potent drivers of resistance than previously thought

Teacher Hiring Practices

Of the various school-based determinants of student achievement, teachers are the most important (Rivkin, Hanushek, and Kain, 2005; Aaronson, Barrow, and Sander 2007; Kane, Rockoff, and Staiger, 2008). Further, teachers who boost student test scores the most are also able to improve adult outcomes of their students (Chetty et al., 2011; Chetty, Friedman, and Rockoff, 2014). There are a number of potential mechanisms for increasing teacher quality, such as improving current teachers’ skills, enhancing teachers’ incentives to maximize their performance, and differentially retaining superior teachers. This brief focuses on another promising avenue for enhancing teacher quality—improving the teacher hiring process. The Metro Atlanta Policy Lab for Education investigated the characteristics principals emphasize and undervalue during the teacher hiring process and found some characteristics were better than others at predicting teacher performance

Psychological Evaluation of Parenting Capacity in Child Welfare Proceedings

In the child welfare context, courts, attorneys, and child protection agencies often turn to psychologists to evaluate parenting capacity. As evaluators in child protection cases, psychologists may be asked to evaluate different parties for different purposes, acting as agents of the court, the child protection agency, or directly retained by the parents or the lawyer guardian ad litem. In this article we focus specifically on psychological evaluations addressing issues pertaining to parenting capacity (in contrast to, for example, assessments that focus solely on child psychological well-being or developmental status). These types of assessments may help to inform dispositional decisions, including placement, visitation, reunification services to be provided, or termination of parental rights. We aim to (1) clarify the uses, and limitations, of such assessments in child protective proceedings, (2) provide an overview of professional guidelines regarding psychological evaluations in child protection matters, along with criteria for evaluating whether the assessment meets these guidelines, and (3) briefly identify broader systems issues surrounding psychological evaluations

OptiMal-PK: an internet-based, user-friendly interface for the mathematical-based design of optimized anti-malarial treatment regimens.

BACKGROUND The search for highly effective anti-malarial therapies has gathered pace and recent years have seen a number of promising single and combined therapies reach the late stages of development. A key drug development challenge is the need for early assessment of the clinical utility of new drug leads as it is often unclear for developers whether efforts should be focused on efficacy or metabolic stability/exposure or indeed whether the continuation of iterative QSAR (quantitative structure-activity and relationships) cycles of medicinal chemistry and biological testing will translate to improved clinical efficacy. Pharmacokinetic and pharmacodynamic (PK/PD)-based measurements available from in vitro studies can be used for such clinical predictions. However, these predictions often require bespoke mathematical PK/PD modelling expertise and are normally performed after candidate development and, therefore, not during the pre-clinical development phase when such decisions need to be made. METHODS An internet-based tool has been developed using STELLA(®) software. The tool simulates multiple differential equations that describe anti-malarial PK/PD relationships where the user can easily input PK/PD parameters. The tool utilizes a simple stop-light system to indicate the efficacy of each combination of parameters. This tool, called OptiMal-PK, additionally allows for the investigation of the effect of drug combinations with known or custom compounds. RESULTS The results of simulations obtained from OptiMal-PK were compared to a previously published and validated mathematical model on which this tool is based. The tool has also been used to simulate the PK/PD relationship for a number of existing anti-malarial drugs in single or combined treatment. Simulations were predictive of the published clinical parasitological clearance activities for these existing therapies. CONCLUSIONS OptiMal-PK is designed to be implemented by medicinal chemists and pharmacologists during the pre-clinical anti-malarial drug development phase to explore the impact of different PK/PD parameters upon the predicted clinical activity of any new compound. It can help investigators to identify which pharmacological features of a compound are most important to the clinical performance of a new chemical entity and how partner drugs could potentially improve the activity of existing therapies

Incorporating genetic selection into individual‐based models of malaria and other infectious diseases

Introduction Control strategies for human infections are often investigated using individual‐based models (IBMs) to quantify their impact in terms of mortality, morbidity and impact on transmission. Genetic selection can be incorporated into the IBMs to track the spread of mutations whose origin and spread are driven by the intervention and which subsequently undermine the control strategy; typical examples are mutations which encode drug resistance or diagnosis‐ or vaccine‐escape phenotypes. Methods and results We simulated the spread of malaria drug resistance using the IBM OpenMalaria to investigate how the finite sizes of IBMs require strategies to optimally incorporate genetic selection. We make four recommendations. Firstly, calculate and report the selection coefficients, s, of the advantageous allele as the key genetic parameter. Secondly, use these values of “s” to calculate the wait time until a mutation successfully establishes itself in the pathogen population. Thirdly, identify the inherent limits of the IBM to robustly estimate small selection coefficients. Fourthly, optimize computational efficacy: when “s” is small, fewer replicates of larger IBMs may be more efficient than a larger number of replicates of smaller size. Discussion The OpenMalaria IBM of malaria was an exemplar and the same principles apply to IBMs of other diseases

Fetal Load and the Evolution of Lumbar Lordosis in Bipedal Hominins

Anthropolog

Pharmacological modelling to investigate antimalarial drug treatment

Malaria remains a major public health concern for billions of people worldwide. Achieving the ambitious goal of malaria eradication requires co-ordination of control strategies dealing with a range of parasite, vector, human, social and environmental factors. Availability of effective antimalarial treatment is a key component in malaria control. However the number of drugs available is limited and drug resistance, particularly in Plasmodium falciparum, has now been reported for all currently available antimalarials. Mathematical models provide the opportunity to explore key features underlying antimalarial drug action, effectiveness and resistance. They further allow investigation into questions that cannot otherwise be easily addressed, either because they are too expensive, unethical or logistically too complex. This thesis aims to develop pharmacological models to investigate antimalarial drug treatment. In Chapter 2 we develop a pharmacokinetic-pharmacodynamic (PK/PD) model of antimalarial drug treatment (calibrated using published data) and use it to investigate the efficacy of artemisinin combination therapies (ACTs). Chapter 3 addresses two assumptions built into the methodology that limit the models future application. The model now allows for (i) time lags and drug concentration profiles for drugs absorbed across the gut wall and, if necessary, converted to another active form (ii) multiple drugs within a treatment regimen (iii) differing modes of drug action in combinations (iv) modelling drugs converted to an active metabolite with similar modes of action. In Chapter 4 we extend the methodology to allow for i) the presence of more than one clone when treatment begins (ii) the acquisition of new clones during treatment follow-up (iii) the tracking of individual clones using molecular markers. We then use these extensions to simulate clinical trial data to determine the best methods of analysis. Chapter 5 details how the drug action components of the extended PK/PD model were incorporated into OpenMalaria; a mathematical model of malaria epidemiology allowing investigation of the effects of various intervention strategies including malaria vaccines, vector control strategies and antimalarial drug treatment. In Chapter 6 we investigate the ability of clinical trials to accurately estimate (WoS) using the extended PK/PD model. Windows of selection (WoS) are often used to quantify the genetic process whereby parasites evolve increasing tolerance to antimalarial drugs. We noted a conspicuous lack of comprehensive, good-quality PK datasets currently available in the literature. Despite this, the models produced results highly consistent with field data. They were applied to investigate the potential implications of drug resistance and to make predications about the future effectiveness of antimalarials. We emphasise the value of mathematical models by simulating ‘field data’ to assess the best methods of analysing clinical trials and to investigate the predictive ability of WoS. While we do not suggest models can replace the information gained in clinical trials, this work does demonstrate the importance of mathematical models capable of generating results consistent with field data

Lesser Snow Geese, Chen caerulescens caerulescens, and Ross's Geese, Chen rossii, of Jenny Lind Island, Nunavut

We surveyed the Lesser Snow (Chen caerulescens caerulescens) and Ross’s geese (Chen rossii) of Jenny Lind Island, Nunavut, using aerial photography in June 1988, 1998, and 2006, and a visual helicopter transect survey in July 1990. The estimated number of nesting geese was 39 154 ± SE 2238 in 1988, 19 253 ± 2323 in 1998, and 21 572 ± 1898 in 2006. In 1988 an estimated 2.7% of the nesting geese were Ross’s. The July 1990 population of adult-plumaged birds was 25 020 ± 3114. The estimated percentage blue morph among Snow and Ross’s geese was 19.0% in 1988, 25.1% in 1989, 23.0% in 1990 and 21.1% in 2006. Estimated pre-fledged Snow Goose productivity was 47% young in 1989 and 46% in 1990. Combined numbers of Snow and Ross’s geese on Jenny Lind Island grew over 250 fold, from 210 adults in 1962-1966 to 54 100 adults in 1985. Numbers subsequently declined, to 42 200 in 1988, 25 000 in 1990, 20 300 in 1998, and 26 400 in 2006. Population decline between 1985 and 1990 was consistent with anecdotal reports by others that die-offs of Snow Geese occurred in 1984, 1985 and 1989, and with our August 1989 fieldwork which found evidence of habitat degradation and malnourishment of young geese. In spite of limited food resources on Jenny Lind Island, the colony continued to exist in 2006 at near its 1990 and 1998 levels. Further studies there could provide insights for management of the overabundant mid-continent Snow Goose population and its arctic habitats

A Computer Modelling Approach To Evaluate the Accuracy of Microsatellite Markers for Classification of Recurrent Infections during Routine Monitoring of Antimalarial Drug Efficacy

Anti-malarial drugs have long half-lives, so clinical trials to monitor their efficacy require long durations of follow-up to capture drug failure that may only become patent weeks after treatment. Reinfections often occur during follow-up so robust methods of distinguishing drug failures (recrudescence) from emerging new infections are needed to produce accurate failure rate estimates. "Molecular correction" aims to achieve this by comparing the genotypes between a patient's pre-treatment (initial) blood sample and any infection that occurs during follow-up, 'matching' genotypes indicating a drug failure. We use an in-silico approach to show that the widely used "match counting" method of molecular correction with microsatellite markers is likely to be highly unreliable and may lead to gross under- or over-estimates of true failure rates depending on the choice of matching criterion. A Bayesian algorithm for molecular correction has been previously developed and utilized for analysis of in vivo efficacy trials. We validated this algorithm using in silico data and showed it had high specificity and generated accurate failure rate estimates. This conclusion was robust for multiple drugs, different levels of drug failure rate, different levels of transmission intensity in the study sites, and microsatellite genetic diversity. The Bayesian algorithm was inherently unable to accurately identify low-density recrudescence that occurred in a small number of patients, but this did not appear to compromise its utility as a highly effective molecular correction method for analysing microsatellite genotypes. Strong consideration should be given to using Bayesian methodology for obtaining accurate failure rate estimates during routine monitoring trials of antimalarial efficacy that use microsatellite marker

A Prospective Study of Prepregnancy Dietary Iron Intake and Risk for Gestational Diabetes Mellitus

OBJECTIVE It is important to identify modifiable factors that may lower gestational diabetes mellitus (GDM) risk. Dietary iron is of particular interest given that iron is a strong prooxidant, and high body iron levels can damage pancreatic β-cell function and impair glucose metabolism. The current study is to determine if prepregnancy dietary and supplemental iron intakes are associated with the risk of GDM. RESEARCH DESIGN AND METHODS A prospective study was conducted among 13,475 women who reported a singleton pregnancy between 1991 and 2001 in the Nurses’ Health Study II. A total of 867 incident GDM cases were reported. Pooled logistic regression was used to estimate the relative risk (RR) of GDM by quintiles of iron intake controlling for dietary and nondietary risk factors. RESULTS Dietary heme iron intake was positively and significantly associated with GDM risk. After adjusting for age, BMI, and other risk factors, RRs (95% CIs) across increasing quintiles of heme iron were 1.0 (reference), 1.11 (0.87–1.43), 1.31 (1.03–1.68), 1.51 (1.17–1.93), and 1.58 (1.21–2.08), respectively (P for linear trend 0.0001). The multivariate adjusted RR for GDM associated with every 0.5-mg per day of increase in intake was 1.22 (1.10–1.36). No significant associations were observed between total dietary, nonheme, or supplemental iron intake and GDM risk. CONCLUSIONS These findings suggest that higher prepregnancy intake of dietary heme iron is associated with an increased GDM risk