Towards the rational use of antibiotics: Utilising pharmacometric approaches to improve meropenem and piperacillin treatment in critically ill patients

In 1909 the discovery of the antibiotic arsphenamin marked the beginning of a new era in treating potentially deadly bacterial infections. In the following decades, the discovery of various new antibiotic drugs substantially contributed to a rise in life expectancy from 47.0 to 78.8 years in the United States of America. Despite this considerable progress in treating infectious diseases, bacterial infections remain a major threat to public health. Especially vulnerable patient populations, like critically ill patients, continued to suffer under mortality rates up to 60%. Worryingly, the described achievements are threatened by two alarming developments: While no truly novel antibiotic classes have been discovered and developed in the last three decades, the emergence and spread of antimicrobial resistance -accelerated by the inappropriate use of antibiotic drugs - steadily reduces the efficacy of currently available drugs. As a response to this new challenge, several national and international action plans call not only for a determined search for new antimicrobial drugs, but also for a more rational use of existing antibiotics. One vital component of rational antibiotic drug therapy is an adequate drug exposure at the site of infection, facilitated by the selection of suitable antibiotic drug(s) in combination with an appropriate dosing regimen. The antibiotic drug administered to the patient should be selected based on its efficacy against the pathogen causing the infection. Unfortunately, the pathogen causing the infection is often unknown at the start of antibiotic therapy. As a consequence, broad spectrum antibiotics – like meropenem and piperacillin/tazobactam - are frequently administered to increase the likelihood of an effective therapy. The selection of an appropriate dosing regimen can be complicated and is especially challenging in critically ill patients: The broad range of pathophysiological changes observed in this patient population leads to high pharmacokinetic (PK) variability, which results in substantial differences in drug exposures between patients receiving the same antibiotic drug and dosing regimen. Under the concept of model-informed precision dosing (MIPD), population pharmacokinetic/pharmacodynamic models and patient-specific data (e.g. patient characteristics, drug measurement(s)) can be leveraged to inform and improve dosing decisions in this vulnerable patient population. The objective of the presented thesis was the development, implementation and evaluation of MIPD tools for antibiotic drugs in critically ill patients. To enable the successful integration of MIPD into clinical practice an iterative, integrative and translational approach was followed. The initial and central question ’Is the current antibiotic dosing appropriate?’, was iteratively addressed integrating expertise from a diverse interprofessional team of healthcare professionals and can be segmented into four intermediate steps, all vital to the main objective. First, and as a prerequisite both for model development/evaluation and dosing adaptation, the establishment of a reliable and frequent antibiotic concentration measurement program was required. Second, the collected data was analysed employing pharmacometric and statistical methodology to characterise population PK/pharmacodynamics (PD) and local factors influencing antibiotic therapy (e.g. local pathogen susceptibility). Third, the gained scientific knowledge was translated into easy-to-use, model-informed dosing tools and comprehensive dosing strategies optimised for clinical practice. And fourth, the developed model-informed dosing tools were implemented into clinical routine and subsequently evaluated and optimised. This thesis focused on meropenem and the fixed drug combination piperacillin/tazobactam and addressed individual or multiple of these four steps in three different projects. In Project I, a possible adsorption of the antibiotic meropenem at the cytokine adsorber CytoSorb®, its effect on meropenem exposure and possible consequences for an adequate meropenem dosing were investigated. Despite the absence of clear evidence for a beneficial effect on patients outcomes, the CytoSorb® filter is increasingly used to reduce circulating cytokines in patients experiencing sepsis. Due to its unspecific binding and therefore elimination of molecules up to a molar mass of 55 kDa, concerns have been raised that the CytoSorb® filter unintentionally adsorbs various drugs including meropenem. To investigate if meropenem dosing needs to be increased during CytoSorb® treatment, a nonlinear mixed-effects (NLME) modelling and simulation approach was employed: A population pharmacokinetic model was developed and three distinct approaches to assess if meropenem clearance differed without or during CytoSorb® treatment were applied: (i) quantification of a possible proportional increase in clearance during CytoSorb® treatment (ii) investigation of (non)saturable adsorption at the CytoSorb® filter using different adsorption submodels and (iii) model parameter re-estimation excluding samples collected during CytoSorb® treatment and evaluating the predictive performance for meropenem concentrations during CytoSorb® treatment. In contrast to the expectation of meropenem being adsorped at the CytoSorb® filter, no significant (p<0.05) or relevant effect of CytoSorb® treatment on meropenem exposure was observed. Consequently, neither additional dosing nor a more frequent drug concentration monitoring of meropenem is necessary during the application of CytoSorb® therapy. Project II focused on improving meropenem and piperacillin/tazobactam treatment for critically ill patients at the Charité-Universitätsmedizin Berlin. For this purpose, a 3-staged clinical study was initiated as a coordinated intervention. In stage I, a frequent and reliable concentrations measurement program was implemented to evaluate the current antibiotic therapy. The assessment of the current antibiotic therapy provided insights about local pathogen susceptibility, while highlighting the need for dose individualisation based on patient characteristics: The majority (>90%) of observed pathogens were susceptible to the two administered antibiotic drugs, but target range attainment (minimum antibiotic drug concentrations between 1 and 5 times minimum inhibitory concentration (MIC) of the pathogen) was low for the observed drug concentrations (meropenem: 35.7%, piperacillin: 50.5%) and highly variable between patients with different renal functions. To improve initial meropenem dosing (i.e. prior to the first concentration measurement) and to exploit the newly gained information about the local pathogen susceptibility, a tabular model-informed dosing tool was developed and implemented in stage II of the study. For the development of the tool, an appropriate meropenem PK model was selected from literature and successfully evaluated using the local clinical data. The PK model was then used to conduct stochastic simulations investigating clinically relevant dosing regimens, possible clinical scenarios and the probability of the dosing regimens to achieve adequate drug exposures. To inform dosing prior to pathogen identification, the local pathogen-independent mean fraction of response (LPIFR) was introduced: The LPIFR characterises the probability of a dosing regimen to reach a defined target, e.g. time above the MIC, if only the underlying MIC distribution at a hospital and not the individual MIC of the pathogen causing the infection is known. To inform dosing after MIC value determination, probability of target attainment analyses (PTA) were performed. Dosing recommendations achieving PTA>90% or LPIFR>90% for patients with different creatinine clearances (10.0-300 mL/min) were derived and summarised in one concise and clear table. To assess the potential of the newly developed model-informed dosing tool prior to implementation, the total daily dose of the dosing regimens recommended by the dosing tool for the local study population was compared to the total daily dose of the actually administered dosing regimens. For 77% of the patients with meropenem concentrations outside the target range, the dosing tool suggested a change in daily dose, highlighting the potential of the tool to optimise dosing regimens. To integrate patient individual antibiotic drug measurements and allow for more user flexibility, an interactive model-informed dosing software termed ‘DoseCalculator’ was developed for stage III of the study. In addition to the meropenem PK model already evaluated for stage II of the study, different piperacillin/tazobactam models were extracted from literature, evaluated using the local clinical data collected in stage I of the study and the best performing model implemented into the tool. Based on available knowledge about the infection, three possibilities to calculate the probability of a dosing regimen to reach adequate antibiotic exposures were integrated into the tool: (i) the LPIFR if neither the pathogen nor the MIC is available, (ii) the cumulative fraction of response (CFR) based on the MIC distribution of a specific pathogen if the pathogen is available and (iii) the PTA if the MIC is available. Furthermore, employing a maximum a-posterior (MAP) estimation approach the observed antibiotic drug measurement(s) of a patient can be used in the DoseCalculator to derive patient individual parameter estimates. If drug measurement(s) of a patient are supplied, all analyses and the resulting recommended dosing regimen are based on the individual parameter estimates of the patient. Compared to the observed dosing in stage I, the recommendations of the DoseCalculator led to a substantial relative increase in predicted target attainment (322% meropenem, 505% piperacillin) while reducing the daily dose (median reduction: 77.8% meropenem, 83.4% piperacillin). In Project III the MeroRisk Calculator, an easy-to-use Excel tool to determine the risk of meropenem target non-attainment after standard dosing previously developed at our department, was evaluated using clinical routine data. Since the direct evaluation of the MeroRisk Calculator was not feasible with the available retrospective clinical dataset, a two-step data- and model-based evaluation was conducted: In step one, a meropenem PK model was successfully evaluated using the clinical data. In step two, the evaluated PK model was used as a benchmark for the drug concentration and risk predictions of the MeroRisk Calculator. Compared to the successfully evaluated compartmental PK model, the MeroRisk Calculator provided an equally good and reliable risk assessment (Lin’s concordance correlation coefficient = 0.99) for patients with maintained renal function (creatinine clearance > 50 mL/min). However, for patients with creatinine clearances below 50 mL/min significant deviations were observed. As a consequence, the MeroRisk Calculator should not be used in patients with (severe) renal impairment. In addition to the successful evaluation, the functionality of the MeroRisk Calculator was extended. Based on CFR analysis and EUCAST reported MIC value distributions, risk of target non-attainment can now be assessed depending on the infecting pathogen informing dosing decisions prior to MIC value determination. To conclude, the presented thesis contributed to an individualised and more rational antibiotic drug therapy in critically ill patients. While PK modelling was employed in Project I to exclude a clinically relevant adsorption of meropenem at the CytoSorb® filter, Project II and Project III represent a successful example on development, implementation and evaluation of MIPD tools. As a next step, both the tabular model-informed dosing tool and the DoseCalculator should be prospectively evaluated at Charité-Universitätsmedizin Berlin. The results from this evaluation in particular and this thesis demonstrate the potential of MIPD using comprehensive examples on how to develop, implement and evaluate model-informed dosing tools and contribute to the accelerating implementation of MIPD into clinical practice

Evaluation of a 3M Petrifilm on-farm milk culture system for use in selective dry cow therapy

As an alternative to blanket dry cow therapy (BDCT), selective dry cow therapy (SDCT) is considered a more judicious approach to antimicrobial use for the purpose of mastitis control during the non-lactating period. The purpose of this research was to evaluate the utility of a 3M Petrifilm-based on-farm milk culture system (OFCS) for use in a SDCT program. A randomized clinical trial was conducted using 16 low bulk tank somatic cell count (<250,000 cells/mL) dairy herds from Prince Edward Island (n = 10) and Quebec (n = 6), Canada. When used to detect intramammary infection (IMI) in cows at drying off, the OFCS performed well with a sensitivity of 85% and specificity of 73%. By comparing producer-derived Petrifilm results to those obtained using an accurate automated reader, it was determined that producers were able to correctly interpret Petrifilm results with an observed agreement of 91% and kappa value of 0.82. The study groups comprising the clinical trial were a positive control group consisting of cows receiving blanket application of dry cow therapy (DCT) with the addition of an internal teat sealant (ITS), and the OFCS group consisting of cows selectively treated at drying off based on OFCS results with ITS alone (Petrifilm negative) or DCT + ITS (Petrifilm positive). No significant differences in postcalving IMI risk and risk of clinical mastitis in the first 120 days of the subsequent lactation were detected between the study groups. Furthermore, no significant differences were observed between study groups regarding test day milk yield and somatic cell count in the first 180 days of the subsequent lactation. Petrifilm-OFCS-based SDCT enabled a reduction in DCT of 21% as compared to BDCT. The effect of ITS on the interdependence of quarters towards the acquisition of new IMI (NIMI) with coagulase negative staphylococci (CNS) over the dry period was also investigated. It was demonstrated that in cows that were infused with ITS, the presence of CNS in another quarter at drying off was a risk factor for CNS NIMI, but this association was absent in cows without ITS infusion. This unexpected finding may have resulted from inadvertent introduction of CNS during ITS infusion. In quarters of cows without a CNS IMI at drying off (i.e. without a source of contagious CNS), ITS was protective against CNS NIMI suggesting that CNS infection over the non-lactating period can be partly attributed to CNS species located within the environment. According to economic analyses, OFCS-based SDCT resulted in a marginally higher total cost per cow than BDCT. However, OFCS-based SDCT provided the combined benefits of lowering DCT treatment risk without increasing the risk of postcalving IMI, and was superior economically to a SDCT program based on somatic cell count and clinical mastitis history. Overall, the Petrifilm OFCS was an accurate diagnostic tool that was effective in reducing DCT use without compromising the health, welfare, and future milk production of the cow

Epidemiologic approach to antibiotic dry cow therapy in dairy herds

The nonlactating period is a critically important time for dairy cow health and optimal subsequent milk yield. Regarding udder health, at the beginning and end of this period, cows are most susceptible to new intramammary infections. In addition, dry-off is also the optimal time to cure any existing mammary-gland infections. Hence, intramammary-infused antibiotic dry cow therapy (DCT) after final milking is an important and widely used mastitis control measure. DCT can be administered either to all quarters of all cows or only to infected cows or quarters. The global antibiotic resistance problem causes an increasing need to reduce antibiotic use in livestock, but a shift in management should not harm animal welfare or impair farm profitability. Although selective treatment of only infected cows is a more sustainable approach, accurate and cost-effective identification of cows or quarters in need of medication is a challenge. The objective of this epidemiologic study was to determine ideal drying-off practices to maintain good udder health and productivity while implementing prudent use of antibiotics. The specific focus of interest was on DCT, and an additional aim was to examine risk factors for post-calving udder health problems and reduced milk yield. The three cohort studies comprised retrospective herd- and cow-level dairy-herd-recording data registered from conventional, commercial Finnish dairy farms. The first study evaluated herd-level associations between milk somatic cell count (SCC), milk yield, and various farm characteristics, with an emphasis on antibiotic usage at dry-off. The second study investigated whether the herd-level DCT practice was associated with cow-level udder health in early lactation. The third study determined cow-level milk yield and SCC differences within the first half of lactation between selectively DCT-treated and -untreated cows. Results show that herd-level selective-DCT strategy, even with a very moderate proportion of medicated cows, is no hindrance to maintenance of low herd-average SCC and good milk yield. Regardless of the farm’s DCT practice, average milk yield increased over time, while average SCC remained rather constant. However, the large variation between farms in average yield and SCC over DCT practices suggests the need for farm-specific protocols. The practice of treating all cows in a herd was associated with lower test-day SCC within early lactation compared with the selective-DCT practice. Moreover, a DCT-treated cow on a selective-DCT farm had lower SCC after calving than did an untreated cow. This indicates that DCT continues to be an effective mastitis control practice. Most selective-DCT farms administered DCT to only one-third of their cows, or less. The DCT effect on milk yield differed depending on late-lactation SCC, so that a higher SCC near dry-off led to an increased difference in yield between treated and untreated cows within the first half of lactation. A missed DCT for a high-SCC cow thus had an adverse effect on subsequent lactation milk yield and on SCC, highlighting the need for accurate selection of cows to be treated. Risk factors associated with higher post-calving SCC included high late-lactation SCC, lactational mastitis treatment, high parity, high preceding-lactation average SCC, and high milk yield near dry-off. Rising awareness of the selective-DCT approach currently is emerging in those countries where it is common to treat all cows at the end of lactation. The successful implementation of selective DCT on Finnish farms will hopefully encourage these countries to gradually reduce antibiotic use in dairy livestock. Such a development would perhaps have a positive effect on consumer confidence in the dairy industry. On Finnish dairy farms, rapid structural change in agriculture continues, and therefore maintaining the current low consumption of antibiotics requires optimal farm management, professional advice, and active monitoring.Umpikausi on lypsylehmän tulevan terveyden ja optimaalisen maidontuotannon kannalta kriittisen tärkeä tuotantokierron vaihe. Lehmät ovat kaikkein alttiimpia uusille utaretulehduksille heti umpeutuksen jälkeen ja juuri ennen poikimista. Lisäksi umpeutusvaihe on paras aika lääkitä jo olemassa olevia utaretulehduksia. Tämän vuoksi umpihoito antibioottia sisältävillä, utareen sisäisesti annosteltavilla lääkkeillä lypsykauden lopuksi on tehokas ja yleinen keino vähentää utaretulehduksen esiintyvyyttä. Umpihoito voidaan antaa karjan kaikille lehmille tai valikoidusti vain piilevää utaretulehdusta sairastaville lehmille. Maailmanlaajuisen antibioottiresistenssiongelman myötä on tarve vähentää antibioottien käyttöä kotieläinten hoidossa. Valikoidusti toteutettu umpihoito on vastuullisempi toimintatapa, mutta sen haasteena on umpihoitoa tarvitsevien lehmien tunnistaminen tarkasti ja kustannustehokkaasti. Tämän epidemiologisen tutkimuksen tavoitteena oli kartoittaa umpeutuskäytäntöjä, joilla saavutetaan hyvä utareterveys ja maidontuotanto vastuullisesti antibiootteja käyttäen. Tutkimuksen erityisen kiinnostuksen kohteena olivat lääkkeelliset umpihoidot. Lisäksi tutkimus kartoitti umpeutukseen liittyviä riskitekijöitä, jotka altistavat utareterveysongelmille sekä alentuneelle maidontuotannolle umpikauden jälkeen. Tutkimuksen aineisto koostui suomalaisten lypsykarjatilojen karja- ja lehmäkohtaisista tuotosseurantatiedoista. Ensimmäinen osatutkimus kartoitti umpihoitokäytäntöjen ja erilaisten tilaominaisuuksien yhteyksiä lypsykarjojen maidon keskisolulukuun ja keskituotokseen. Toinen osatutkimus selvitti tilojen umpihoitokäytäntöjen yhteyksiä lehmien alkulypsykauden utareterveyteen. Kolmas osatutkimus kartoitti valikoidusti umpilääkittyjen ja lääkitsemättömien lehmien välisiä eroja maitotuotoksessa ja soluluvussa seuraavan lypsykauden alkupuoliskolla. Tulokset osoittavat, että jopa hyvin maltillisella umpihoidettujen lehmien osuudella voidaan saavuttaa karjan hyvä utareterveys ja keskituotos. Riippumatta tilan umpihoitokäytännöstä, tarkasteltujen vuosien aikana maidon keskituotos nousi, ja maidon keskisoluluku pysyi melko muuttumattomana. Tilojen väliset erot keskituotoksessa ja -soluluvussa olivat suuria, mikä viittaa tilakohtaisten umpeutusohjeiden tarpeeseen. Verrattuna valikoivaan umpihoitokäytäntöön, karjan kaikkien lehmien lääkitseminen oli yhteydessä lehmien alhaisempaan solulukuun alkulypsykaudella. Tiloilla, jotka toteuttivat valikoivaa umpihoitokäytäntöä, poikimisen jälkeinen soluluku oli alhaisempi lääkityillä kuin lääkitsemättömillä lehmillä. Tämän perusteella umpihoito on edelleen tehokas utaretulehduksen torjuntakeino. Suurin osa valikoivasti hoitavista maidontuottajista lääkitsi enintään kolmanneksen lehmistään. Umpihoidon vaikutus tuotokseen vaihteli loppulypsykauden soluluvun mukaan. Loppulypsykaudella korkean soluluvun omaavan lehmän lääkitsemättä jättäminen vaikutti haitallisesti myöhempään maitotuotokseen ja utareterveyteen, mikä korostaa hoidettavien lehmien tarkan valinnan merkitystä. Umpeutukseen liittyviä utaretulehdusongelmien riskitekijöitä olivat loppulypsykauden korkea soluluku, aiemmin sairastettu ja lääkehoitoa vaatinut utaretulehdus, korkea ikä, edeltävän lypsykauden korkea keskisoluluku ja korkea päivämaitotuotos lähellä umpeutusta. Maissa, joissa on tavanomaista lääkitä kaikki lehmät lypsykauden lopuksi, tiedostetaan enenevässä määrin, että valikoiva hoito on vastuullisempi hoitokäytäntö. Toivottavasti suomalaisilla lypsykarjatiloilla onnistuneesti toteutettu valikoiva umpihoito toimii kannustavana esimerkkinä kansainvälisille pyrkimyksille vähentää antibioottien käyttöä. Maatalouden nopea rakennemuutos jatkuu suomalaisilla maitotiloilla, ja vallitsevan, vähäisen antibioottikulutuksen ylläpitäminen edellyttää ihanteellista maatilanhoitoa, ammattitaitoista neuvontaa ja aktiivista seurantaa

Population genetics of Streptococcus pneumoniae: the response to antibiotic and vaccine pressure

Streptococcus pneumoniae (pneumococcus) is a pathogen and a commensal of the upper respiratory tract, which is a leading cause of child mortality. The development of a successful vaccine against pneumococcal carriage and disease and the ongoing challenge of antimicrobial resistant strains mean that there is an imperative to understand how the pneumococcal population responds to vaccine and antibiotic pressure. Strains of pneumococci with reduced susceptibility to penicillin and other antibiotics have emerged and are a cause of concern, though their clinical impact is unclear. I perform a meta-analysis to examine the impact of antibiotic non-susceptibility on the risk of mortality and show that a meningitis infection with penicillin non-susceptible pneumococci increases the risk of mortality two fold. I then examine why some serotypes of pneumococcus are more likely to carry resistance than others. Using a mathematical model I generate the hypothesis that serotypes with a long duration of carriage are more likely to have a high prevalence of antibiotic resistance than those with a short duration of carriage. Using maximum likelihood estimation, I show that in children less than two years of age, penicillin resistance only occurs in those serotypes whose duration of carriage is nineteen days or more. Having considered the circumstances under which antibiotic resistance carries a selective advantage in a pneumococcal serotype, I then consider the effect that the spread of an advantageous gene has on the genetic diversity in a generalised bacterial population. Most simple models predict that when a novel allele arises in a bacterial population that is fitter than other alleles at that locus, it and its descendents will increase in frequency and sweep to fixation in the population. In the absence of recombination, all genetic diversity at all loci other than those within the sweeping genotype is lost. I consider whether asymmetric recombination can prevent the loss of diversity over the whole genome. I show that asymmetric recombination, when occurring at rates estimated to date from bacterial populations, is not frequent enough to prevent the extinction of alleles from the wild-type population. Finally, I analyse sequence data sampled from carried pneumococci to examine the impact of vaccination on genetic diversity in a pneumococcal population, an example of a selective event in a pneumococcal population

The Impact of Collateral Evolution on Optimal Dosing Strategies and Evolution on Paired Fitness Landscapes

Drug resistance is an ever-growing threat to successful treatment of bacterial, cancer and viral infections. As pathogens and cancers continue to find evolutionary solutions to the drugs we treat them with, scientists have begun to focus on more evolutionary-based therapies such as drug cycling. These therapies aim to constrain or control evolution in a particular way such that intractable resistance never evolves. In the same vein, recent work has revealed collateral sensitivity as a promising avenue to guide evolution away from untreatable resistance states. Collateral evolution occurs when a population evolves resistance to the selecting drug and this mechanism of resistance confers "collateral" effects to different drugs it is not exposed to. In this work we show how collateral profiles might be used to slow the acquisition to resistance in a simplified laboratory-based evolution experiment. We demonstrate that intuitive cycling protocols often fail over long time periods, whereas mathematically optimized protocols maintain long-term sensitivity at the cost of transient periods of high resistance. We then extend this work to include nonantibiotic stressors such as pH, salt and food preservatives. This extension highlights that more work is necessary to understand the role these common environments have on the development of multidrug resistance. Finally, using the well-known fitness landscape paradigm, we explore how collateral effects influence the evolutionary dynamics of a pair of landscapes with tunable correlations. We show that alternating evolution in highly correlated environments can lead to higher mean fitness than evolution in either landscape alone, while alternating between two anti-correlated landscapes results in a lower mean fitness. We demonstrate this is due to the location and number of shared maxima between the two correlated landscapes, which change as a function of ruggedness (epistasis) and paired landscape correlation. Taken together, these results begin to answer many of the important questions required to translate collateral sensitivity into clinical treatments.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163088/1/jamaltas_1.pd

Development of a within-host mathematical model of urethral gonorrhoea infection and application to treatment

Background To understand the emergence and spread of drug resistance in the context of Neisseria gonorrhoeae (NG) it is first important to characterise the within-host dynamics that influence these events. However, in the context of NG, the within-host infection process is not well understood, with little investigation into the role of intracellular NG on the progression of natural infection and the dynamics of infection under treatment. This thesis aims to address this gap through the use of novel within-host mathematical models to describe symptomatic male urethral NG infection. Methods Initially, we developed a model to describe the progression of natural infection by considering the interaction between extracellular and intracellular NG with immune response through PMN. This initial model was then adopted to incorporate treatment of NG infection through pharmacokinetic (PK) and pharmacodynamic (PD) approaches for several antibiotics, examining both extracellular and intracellular dynamics after treatment. While this relatively simple treatment model with conventional approaches was successful in explaining the infection dynamics for the non-β-lactam drugs we test, using β-lactams the model struggled to describe infection dynamics consistent with the literature, leading to a revised approach applied to cefixime and ceftriaxone. This new approach incorporated a transition between bacteriostatic and bactericidal effects determined through threshold effects applied to the fraction of drug bound penicillin-binding-proteins. Parameter values were estimated by fitting to existing empirical data where sub-models to reflect different in vitro experimental conditions were adopted. Results Using the natural infection model, we successfully reproduced known phenomena on bacterial load and infection duration and found that the simulated natural infection was mainly driven by intracellular NG with ~80% of the total NG population internalised from day 5 on. In addition, we achieved realistic infection clearance times for each drug considered and simulations showed treatment failure to be largely driven by unsuccessful clearance of intracellular NG. We also identified relevant PK indices at the intracellular level that differentiated treatment success and failure. Although we investigated multiple dose strategies for orally administered drugs (gepotidacin, azithromycin and cefixime), we found little difference in treatment success for a fixed total dose. Discussion In this study we contribute new theoretical results tied to available observations in an area that has had limited experimental attention. In particular, this has involved development of a new mechanistic model of NG infection within-host and incorporation of realistic features of treatment. The study findings mainly emphasise the importance of the role of intracellular NG in prolonging natural infection and determining treatment success. The treatment model facilitates exploration of differing treatment regimens, the link between PK/PD indices and treatment success and where relevant incorporates mechanistic effects of drug-target binding. This work suggests the importance of intracellular infection in both persistence of infection and drug clearance, and presents and opportunity to investigate these predictions in future experiments. The approach taken here is flexible and has the potential to be expanded in various ways, including to other anatomical sites, consider the role of vaccines in clearance and to address the motivating questions around the emergence of drug resistance within-host

Mathematical models of gonorrhoea and chlamydia: Biology, behaviour and interactions

Gonorrhoea and chlamydia are curable, bacterial, sexually transmitted infections (STIs) of humans, with important long term consequences for health. Their epidemiology and biology are reviewed in chapter one. The way the biology of the organisms and the behaviour of human hosts interact to influence the patterns of infection and the potential impact of interventions is the subject of the main body of the thesis. Mathematical models are presented, together with empirical data, to gain a better understanding of the epidemiology of gonorrhoea and chlamydia. New approaches are applied, using more complex measures of disease occurrence including reinfection (subsequent infection by the same organism) or coinfection (infection with both organisms simultaneously). Coinfection with gonorrhoea and chlamydia is investigated in chapter two. The third chapter investigates the importance of heterogeneity in human behaviour (i.e. level of sexual activity, mixing patterns within and between populations) on the spread of disease in subpopulations, using a model incorporating race, gender and sexual activity level. This was parameterised and validated using data collected in South East London. In chapter four, models of reinfection are used to investigate the interaction of population level parameters such as degree of assortative mixing and rates of reinfection. In chapter five, the characteristics of individuals coinfected with both organisms are shown to provide additional information useful in determining how infection is distributed across a population. The biology of the organism is demonstrated, in the fifth chapter, to play an important role in the prevalence and incidence of disease within the host population. The impact of the emergence of resistant or asymptomatic phenotypes under selective pressure by different treatment regimens is quantified using a two strain model, including asymptomatic and symptomatic infections. The final chapter considers the contribution of the research and discusses the implications of the results for STI intervention strategies

Approaching zero : temporal effects of a restrictive antibiotic policy on hospital-acquired Clostridium difficile, extended-spectrum β-lactamase-producing coliforms and meticillin-resistant Staphylococcus aureus

A restrictive antibiotic policy banning routine use of ceftriaxone and ciprofloxacin was implemented in a 450-bed district general hospital following an educational campaign. Monthly consumption of nine antibiotics was monitored in defined daily doses (DDDs) per 1000 patient-occupied bed-days (1000 pt-bds) 9 months before until 16 months after policy introduction. Hospital-acquired Clostridium difficile, meticillin-resistant Staphylococcus aureus (MRSA) and extended-spectrum -lactamase (ESBL)- producing coliform cases per month/1000 pt-bds were identified and reviewed throughout the hospital. Between the first and final 6 months of the study, average monthly consumption of ceftriaxone reduced by 95% (from 46.213 to 2.129 DDDs/1000 pt-bds) and that for ciprofloxacin by 72.5% (109.804 to 30.205 DDDs/1000 pt-bds). Over the same periods, hospital-acquisition rates for C. difficile reduced by 77% (2.398 to 0.549 cases/1000 pt-bds), for MRSA by 25% (1.187 to 0.894 cases/1000 pt-bds) and for ESBL-producing coliforms by 17% (1.480 to 1.224 cases/1000 pt-bds). Time-lag modelling confirmed significant associations between ceftriaxone and C. difficile cases at 1 month (correlation 0.83; P < 0.005), and between ciprofloxacin and ESBL-producing coliform cases at 2 months (correlation 0.649; P = 0.002). An audit performed 3 years after the policy showed sustained reduction in C. difficile rates (0.259 cases/1000 pt-bds), with additional decreases for MRSA (0.409 cases/1000 pt-bds) and ESBL-producing coliforms (0.809 cases/1000 pt-bds). In conclusion, banning two antibiotics resulted in an immediate and profound reduction in hospital-acquired C. difficile, with possible longer-term effects on MRSA and ESBL-producing coliform rates. Antibiotic stewardship is fundamental in the control of major hospital pathogens

Slowing the Evolution and Outbreak of Antibiotic Resistance

Antibiotic resistance is a problem of significant and growing international concern, in part due to the rapid evolution of new resistances. One potentially important factor in the emergence of resistance is concentrated antibiotic use in environments such as hospitals. Such high use creates a strong selective pressure for pathogens to evolve resistance. We analyze some strategies hospitals can use to slow the evolution of resistance, and estimate the length of the delay between evolution and outbreak of resistance