Effects of antibiotic interaction on antimicrobial resistance development in wastewater.

While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the effects of antibiotic interaction. We found that populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on resistance development. Furthermore E. coli populations grown with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments.

Effects of Antibiotic Interaction on Antimicrobial Resistance Development in Wastewater.

While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the complex effects of antibiotic interaction. We found that while E. coli populations grown in additively interacting antibiotic combinations grew predictably according to the previously developed model, those populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown in the condition with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on antimicrobial resistance development. Furthermore E. coli populations grown in the condition with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments. Importance: Antimicrobial resistance (AMR) is a growing global threat to public health expected to impact 10 million people by 2050, driving mortality rates globally and with a disproportionate effect on low- and middle-income countries. Communities in proximity to wastewater settings and environmentally contaminated surroundings are at particular risk due to resistance stemming from antibiotic residues from industrial and agricultural runoff. Currently, there is a limited quantitative and mechanistic understanding of the evolution of AMR in response to multiple interacting antibiotic residues in constant flow environments. Using an integrated computational and experimental methods, we find that interactions between antibiotic residues significantly affect the development of resistant bacterial populations.

PharmaChk: a decade of research and development towards the first quantitative, field-based medicine quality screening instrument.

Biomedical and clinical scientists play a major role in translating observations into interventions - therapeutics, diagnostics, and medical devices including screening instruments - that improve the health of individuals and the public. This path from observation to intervention is often long and beset with obstacles, many unanticipated. We believe that sharing concrete, real-word examples of scientists in academia moving along this path will highlight some of the types of challenges one may face; here we focus on an intervention being developed by the Zaman lab at Boston University - PharmaChk, the first quantitative, field-based instrument for medicine quality screening. Specifically, this paper describes the first ten years of scientific and engineering work towards the development of this instrument. Launched from a need observed by medicine quality scientists, the development of PharmaChk has required the integration of multiple technologies enabled by knowledge and expertise across diverse fields of science and engineering, including chemistry, ultrasonics, fluid dynamics, optics, computer science, and automation. These efforts have been shaped and driven by the many challenges we have faced and the technical, commercial, and financial support that we have received from many collaborators. By sharing this example, we hope to inspire our colleagues to pursue their own paths to new healthcare solutions.

Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater.

Although wastewater and sewage systems are known to be significant reservoirs of antibiotic-resistant bacterial populations and periodic outbreaks of drug-resistant infection, there is little quantitative understanding of the drivers behind resistant population growth in these settings. In order to fill this gap in quantitative understanding of the development of antibiotic-resistant infections in wastewater, we have developed a mathematical model synthesizing many known drivers of antibiotic resistance in these settings to help predict the growth of resistant populations in different environmental scenarios. A number of these drivers of drug-resistant infection outbreak, including antibiotic residue concentration, antibiotic interaction, chromosomal mutation, and horizontal gene transfer, have not previously been integrated into a single computational model. We validated the outputs of the model with quantitative studies conducted on the eVOLVER continuous culture platform. Our integrated model shows that low levels of antibiotic residues present in wastewater can lead to increased development of resistant populations and that the dominant mechanism of resistance acquisition in these populations is horizontal gene transfer rather than acquisition of chromosomal mutations. Additionally, we found that synergistic antibiotics at low concentrations lead to increased resistant population growth. These findings, consistent with recent experimental and field studies, provide new quantitative knowledge on the evolution of antibiotic-resistant bacterial reservoirs, and the model developed herein can be adapted for use as a prediction tool in public health policy making, particularly in low-income settings where water sanitation issues remain widespread and disease outbreaks continue to undermine public health efforts. IMPORTANCE The rate at which antimicrobial resistance (AMR) has developed and spread throughout the world has increased in recent years, and according to the Review on Antimicrobial Resistance in 2014, it is suggested that the current rate will lead to AMR-related deaths of several million people by 2050 (Review on Antimicrobial Resistance, Tackling a Crisis for the Health and Wealth of Nations, 2014). One major reservoir of resistant bacterial populations that has been linked to outbreaks of drug-resistant bacterial infections but is not well understood is in wastewater settings, where antibiotic pollution is often present. Using ordinary differential equations incorporating several known drivers of resistance in wastewater, we find that interactions between antibiotic residues and horizontal gene transfer significantly affect the growth of resistant bacterial reservoirs.

Predicting resource-dependent maternal health outcomes at a referral hospital in Zanzibar using patient trajectories and mathematical modeling.

Poor intra-facility maternity care is a major contributor to maternal mortality in low- and middle-income countries. Close to 830 women die each day due to preventable maternal complications, partly due to the increasing number of women giving birth in health facilities that are not adequately resourced to manage growing patient populations. Barriers to adequate care during the 'last mile' of healthcare delivery are attributable to deficiencies at multiple levels: education, staff, medication, facilities, and delays in receiving care. Moreover, the scope and multi-scale interdependence of these factors make individual contributions of each challenging to analyze, particularly in settings where basic data registration is often lacking. To address this need, we have designed and implemented a novel systems-level and dynamic mathematical model that simulates the impact of hospital resource allocations on maternal mortality rates at Mnazi Mmoja Hospital (MMH), a referral hospital in Zanzibar, Tanzania. The purpose of this model is to provide a rigorous and flexible tool that enables hospital administrators and public health officials to quantitatively analyze the impact of resource constraints on patient outcomes within the maternity ward, and prioritize key areas for further human or capital investment. Currently, no such tool exists to assist administrators and policy makers with effective resource allocation and planning. This paper describes the structure and construct of the model, provides validation of the assumptions made with anonymized patient data and discusses the predictive capacity of our model. Application of the model to specific resource allocations, maternal treatment plans, and hospital loads at MMH indicates through quantitative results that medicine stocking schedules and staff allocations are key areas that can be addressed to reduce mortality by up to 5-fold. With data-driven evidence provided by the model, hospital staff, administration, and the local ministries of health can enact policy changes and implement targeted interventions to improve maternal health outcomes at MMH. While our model is able to determine specific gaps in resources and health care delivery specifically at MMH, the model should be viewed as an additional tool that may be used by other facilities seeking to analyze and improve maternal health outcomes in resource constrained environments.

Small volume method for drug release screening using ultrasonic agitation.

Drug release testing plays a major role along all parts of the dosage form development and manufacturing process. However, official methods to perform this type of testing are often resource intensive and require highly specialized facilities. Affordable and accessible methods for studying drug release behavior are currently lacking. This work presents a small volume approach to solid dissolution and drug release testing of solid dosage forms using ultrasonic agitation. Cavitation and acoustic streaming were generated by a microprobe horn delivering a 40 kHz acoustic signal into a 50 mL test vessel. These two phenomena resulted in breakdown of and release of drug from tablet samples. Prednisone Performance Verification Tablets were used as model tablets to study the effect of system parameters on the drug release process. The effects of these parameters on the acousto-hydrodynamic environment were studied using streak photography and hydrophone measurements. Drug release behavior showed a slow/fast threshold transition separated by a highly variable regime as a function of the system parameters. Observations from drug release experiments and results from acoust-hydrodynamic characterization experiments suggested that this transition is dominated by acoustic streaming. This method represents a screening method to probe relative differences in dosage form composition and acts as a complimentary approach to official testing methods. The small volume format of this test has potential applications in the study of drug release properties from low-dose and novel solid dosage forms as well as reduced cost and increased accessibility of release testing for post-manufacturing tablet quality screening, a current need in low- and middle-income countries.

Modeling patient access to therapeutic oxytocin in Zanzibar, Tanzania.

BACKGROUND: Our objective is to estimate the effects of therapeutic oxytocin supply chain factors and social determinants of health on patient access to oxytocin in low-income settings using system dynamics modeling. Postpartum hemorrhage (PPH), a major cause of maternal mortality disproportionately affects women in low and middle income countries (LMICs). The World Health Organization recommends therapeutic oxytocin as the frontline uterotonic for PPH management and prevention. However, lack of access to quality therapeutic oxytocin in Tanzania, and throughout Sub-Saharan Africa, continues to result in a high number of preventable maternal deaths. METHODS: We used publicly available data from Zanzibar and Sub-Saharan Africa, literature review, oxytocin degradation kinetics and previously developed systems dynamics models to understand the barriers in patient access to quality therapeutic oxytocin. RESULTS: The model makes four basic predictions. First, there is a major gap between therapeutic oxytocin procurement and availability. Second, it predicts that at current population increase rates, oxytocin supply will have to be doubled in the next 30 years. Third, supply and storage temperature until 30 °C has minimal effect on oxytocin quality and finally distance of 5 km or less to birthing facility has a small effect on overall access to oxytocin. CONCLUSIONS: The model provides a systems level approach to therapeutic oxytocin access, incorporating supply and procurement, socio-economic factors, as well as storage conditions to understand how women's access to oxytocin over time can be sustained for better health outcomes.

Controlling uncertainty in aptamer selection.

The search for high-affinity aptamers for targets such as proteins, small molecules, or cancer cells remains a formidable endeavor. Systematic Evolution of Ligands by EXponential Enrichment (SELEX) offers an iterative process to discover these aptamers through evolutionary selection of high-affinity candidates from a highly diverse random pool. This randomness dictates an unknown population distribution of fitness parameters, encoded by the binding affinities, toward SELEX targets. Adding to this uncertainty, repeating SELEX under identical conditions may lead to variable outcomes. These uncertainties pose a challenge when tuning selection pressures to isolate high-affinity ligands. Here, we present a stochastic hybrid model that describes the evolutionary selection of aptamers to explore the impact of these unknowns. To our surprise, we find that even single copies of high-affinity ligands in a pool of billions can strongly influence population dynamics, yet their survival is highly dependent on chance. We perform Monte Carlo simulations to explore the impact of environmental parameters, such as the target concentration, on selection efficiency in SELEX and identify strategies to control these uncertainties to ultimately improve the outcome and speed of this time- and resource-intensive process.

Rapid and specific drug quality testing assay for artemisinin and its derivatives using a luminescent reaction and novel microfluidic technology.

Globally, it is estimated that about 10-30% of pharmaceuticals are of poor quality. Poor-quality drugs lead to long-term drug resistance, create morbidity, and strain the financial structure of the health system. The current technologies for substandard drug detection either are too expensive for low-resource regions or only provide qualitative results. To address the current limitations with point-of-care technologies, we have developed an affordable and robust assay to quantify the amount of active pharmaceutical ingredients (APIs) to test product quality. Our novel assay consists of two parts: detection reagent (probe) and a microfluidic testing platform. As antimalarials are of high importance in the global fight against malaria and are often substandard, they are chosen as the model to validate our assay. As a proof-of-concept, we have tested the assay with artesunate pure and substandard samples (Arsuamoon tablets) from Africa and compared with the conventional 96-well plate with spectrophotometer to demonstrate the quantitative efficacy and performance of our system.

Tackling HIV through robust diagnostics in the developing world: current status and future opportunities.

Over the last thirty years, the world has seen HIV circulate the globe, affecting 33 million people to date and killing 2 million people a year. The disease has affected developed and developing countries alike, and in the U.S., remains one of the top ten leading causes of death. Many regions of the world are highly impacted by this disease, including sub-Saharan Africa, South and South-East Asia, and Eastern Europe. Fortunately, multilateral, global efforts, along with successful developments in diagnostic tools and anti-retroviral drugs (ARVs) have successfully curbed the spread of HIV over the last ten years. In spite of this fact, access to HIV treatment and preventive healthcare is varying and limited in developing countries. A lack of healthcare infrastructure, financial support, and healthcare workers are some logistical factors that are responsible. HIV stigmatization, discrimination, and inadequate education pose additional social challenges that are hindering countries from advancing in HIV prevention. This review focuses on current technological tools that are used for HIV diagnosis and ongoing research that is aimed at addressing the conditions in low-resource settings. Recent developments in microfluidic applications and mobile health technologies are promising approaches to building a compact, portable, and robust device that can provide information-rich, real-time diagnoses. We also discuss the role that governments, healthcare workers, and even researchers can play in order to increase the acceptance of newly introduced devices and treatments in rural communities.