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Quantifying the fitness benefit and cost of cefixime resistance in Neisseria gonorrhoeae to inform prescription policy

Gonorrhea is one of the most common bacterial sexually transmitted infections in England. Over 41,000 cases were recorded in 2015, more than half of which occurred in men who have sex with men (MSM). As the bacterium has developed resistance to each first-line antibiotic in turn, we need an improved understanding of fitness benefits and costs of antibiotic resistance to inform control policy and planning. Cefixime was recommended as a single dose treatment for gonorrhea from 2005 to 2010, during which time resistance increased and subsequently declined. We developed a stochastic compartmental model representing the natural history and transmission of cefixime sensitive and resistant strains of Neisseria gonorrhoeae in MSM in England, which was applied to data on diagnoses and prescriptions between 2008 and 2015. We estimated that asymptomatic carriers play a crucial role in overall transmission dynamics, with about 40% of infections remaining asymptomatic and untreated, accounting for 96% of onward transmission. The fitness cost of cefixime resistance in the absence of cefixime usage was estimated to be such that the number of secondary infections caused by resistant strains is only about half as much as for the susceptible strains, which is insufficient to maintain persistence. However, we estimated that treatment of cefixime-resistant strains with cefixime was unsuccessful in 84% of cases, representing a fitness benefit of resistance. This benefit was large enough to counterbalance the fitness cost when 31% of cases are treated with cefixime, and when more than 51% of cases were treated with cefixime the resistant strain had a net fitness advantage over the susceptible strain. Our findings have important implications for antibiotic stewardship and public health policies, and in particular suggest that cefixime could be used to treat a minority of gonorrhea cases without raising resistance levels.


Introduction
Gonorrhea, caused by the bacterial pathogen Neisseria gonorrhoeae, is one of the most common 2 sexually transmitted infections in England. Incidence has increased year on year since 2008, culminating 3 in over 41,000 cases in 2015 [1]. Around 22,000 of these cases were found in men who have sex 4 with men (MSM), constituting a 20% annual increase. The greatest cause for concern, however, is 5 the rapid growth in antimicrobial resistance. The bacterium has quickly developed resistance to each 6 first-line antibiotic in turn, from penicillin through to cephalosporins, such as cefixime and ceftriaxone [2]. 7 Treatment with ceftriaxone is the last remaining single-drug option in most settings worldwide; however 8 susceptibility is diminishing rapidly [3]. As a result, England and many other countries now recommend 9 treatment of gonorrhea with a dual therapy of ceftriaxone and azithromycin [4]. Ceftriaxone resistance 10 has been detected only sporadically in England; however azithromycin resistance is easily selected for 11 and was prevalent in a recent outbreak [5]. Resistance to azithromycin effectively reduces the current 12 treatment to a monotherapy, making resistance trends increasingly important to monitor against the 13 threat of potentially untreatable gonorrhea. 14 Public Heath England (PHE) runs the Gonococcal Resistance to Antimicrobials Surveillance Programme 15 (GRASP) [68], which has produced a report annually since 2000 [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. GRASP monitors trends in 16 resistance and susceptibility to a panel of antibiotics used to treat gonorrhea in England and Wales, and 17 thus informs national treatment guidelines and strategy. In 2004, GRASP began testing for cefixime 18 resistance, defined as having a Minimum Inhibitory Concentration (MIC) of 0.125 mg/l [10]. In 2005, 19 following worrying increases in resistance to the previous therapy, ciprofloxacin, a new recommendation 20 was introduced that uncomplicated gonorrhea should be treated with a single dose of cefixime [22]. 21 Fig 1 shows the trends in cefixime prescription and resistance in England. Very little resistance was 22 detected until 2007, however by 2009 the total level of resistance had passed the 5% threshold at which 23 the WHO recommends that first-line treatment guidelines should be changed [13,23]. At this time almost 24 60% of gonorrhea diagnoses were being treated with cefixime [15]. The majority of the resistance was 25 concentrated in the MSM population, where it reached a peak of 33% in 2010 [16]. This evidence, 26 combined with increasingly common reports of cefixime treatment failure, formed the basis for the 27 decision in May 2011 for another update to the treatment guidelines for uncomplicated gonorrhea [24,25]. 28 Cefixime was no longer recommended as a first-line treatment, and was replaced with a combination of 29 500mg ceftriaxone and 1g azithromycin [4]. Since 2011, cefixime prescribing has fallen drastically, in 30 line with the updated guidelines. Over the same period the proportion of cefixime-resistant isolates has 31 declined steadily in MSM, falling to less than 1% in 2014 [20]. 32 We hypothesize that the resistance trend observed can be explained by a net fitness benefit to cefixime 33 resistance when cefixime is widely prescribed but a net fitness cost when cefixime prescriptions decline. 34 Understanding the relationship between antibiotic use and the emergence of resistance in gonorrhea has 35 been identified as a key research agenda [26]. Here our main aim is to further the understanding of the 36 evolutionary dynamics of cefixime resistance, and to use this newfound knowledge to inform public health 37 practice. There is still much we do not understand about the natural history of gonorrhea, especially 38 since unobserved asymptomatic infections have long been thought to be an important reservoir of 39 infection. The proportion of incident cases that are asymptomatic at each bodily site of infection is 40 known to vary, but has not been definitively measured [27][28][29]. Furthermore, the expected duration of 41 carriage of asymptomatic gonococcal infection is not well studied. Estimates have been traditionally in 42 the region of six months, however recent work using genomic data on pairs of known sexual contacts 43 has suggested that a longer duration of carriage can occasionally happen [30]. We therefore developed 44 and applied a Bayesian statistical approach to account for these uncertainties in the epidemiology of 45 gonorrhea. 46 47 Epidemiological Data 48 The total number of diagnoses of gonorrhea in MSM in England between 2008 and 2015 were extracted 49 from the Genitourinary Medicine Clinic Activity Dataset (GUMCAD) [60]. This mandatory reporting 50 system provides data on diagnoses of sexually transmitted infections from sexual health services in 51 England, and the GUMCAD data is published annually by PHE on their website at http://www.gov. 52 uk/government/statistics/sexually-transmitted-infections-stis-annual-data-tables. This 53 yearly number of gonorrhea diagnoses is denoted Y (t). 54 The number of cases of gonorrhea in MSM that were cefixime-resistant and reported by GRASP 55 between 2008 and 2015 were extracted from the corresponding GRASP reports [14][15][16][17][18][19][20][21] and denoted 56 Y res (t). The coverage of GRASP was calculated for every year between 2008 and 2015 by taking the 57 ratio between the number of cases included in GRASP (irrespective of resistance) and the number 58 of GUMCAD diagnoses in the same year. This GRASP coverage proportion is denoted q(t). The 59 proportion of gonorrhea cases that were treated with cefixime, as opposed to other antibiotics, was also 60 extracted from the GRASP reports between 2008 and 2015. This time-dependent proportion is denoted 61 ⇡(t) and illustrated in Fig 1.

62
The MSM population is estimated at N = 1.5 million, based on a sexually active male population of 63 20 million [33]. In the third National Survey of Sexual Attitudes and Lifestyles (Natsal) 8.4% of men 64 reported same-sex experience at least once, with 2.6% of men having had a same-sex partner in last 65 five years, putting a plausible range for the MSM population at 0.5 and 1.7 million [34]. Natsal is known 66 to under-represent MSM so an estimate towards the top of the range was adopted [35]. Given the 67 low prevalence of gonococcal infection in the population, the total population size is not expected to 68 excessively affect the results. 69 In order to investigate the fitness cost and benefit of cefixime resistance in gonorrhea, we created a 71 stochastic compartmental model, illustrated in Fig 2 with  of reinfection with gonorrhea have been observed, suggesting low levels of acquired immunity [31], 73 and experimental urethral infection in male volunteers found no protection was conferred on repeat 74 infection with an identical strain six months apart [32]. It was therefore assumed that no immunity was 75 with cefixime (T res;cef ) for whom treatment failure happens and who become asymptomatically infected 92 (A res ) [51,69]. 93 The basic reproduction number, R 0 , is a measure of the reproductive capacity of an infectious agent 95 and is defined as the average number of secondary cases of gonorrhea arising from the introduction of 96 a typical infected individual in a completely susceptible population. Where there is direct competition 97 between strains, as in the situation we are modelling, the strain with the highest R 0 will outcompete the 98 others. 99 To calculate R 0 we must consider the generation-time, defined as the expected time from an individual 100 becoming infected to infecting another individual [37].
Similarly, the basic reproduction number R res 0 for the resistant strains is given by: Bayesian inference 106 We considered the data as a Partially Observed Markov Process, with the number of GUMCAD recorded cases, Y (t), and GRASP reported resistant cases, Y res (t), being the observed realisations of the underlying unobserved processes: the total incidence of gonorrhea infections, Z(t), and incidence of cefixime-resistant infections, Z res (t). The reporting process for the total number of cases recorded by GUMCAD was set to allow for a 10% under-reporting rate on average, with a 10% margin of error: The probability of a cefixime-resistant case of gonorrhea being sampled by the GRASP study was assumed to be Poisson distributed with a sampling probability denoted q(t) derived from the coverage of the GRASP study over 2008 to 2014: Based on these observations we aimed to infer the values of the ten parameters: A sus (0), A res (0), ✓, , , ⌫, 107 ↵, µ, ⇢ and . 108 An analytical expression for the likelihood of the observed data given our model is not available, so 109 we obtained an unbiased estimate of the likelihood using a particle filter [38]. The estimated likelihood 110 was then incorporated into a particle Monte Carlo Markov Chain (pMCMC) which was used to obtain a 111 sample from the posterior distribution of the model parameters [39]. 112 The model fitting was implemented using the R package pomp, which includes a pMCMC algorithm 113 that can be used to perform Bayesian inference [40]. The algorithm was modified to enable parallel 114 computation. The particle filter estimation of the likelihood was based on 1,000 particles, which was 115 sufficiently robust to estimate the likelihood. 6 million iterations of the pMCMC were thinned by a 116 factor of 100. Four separate chains were run with dispersed starting points, and compared using the R 117 package coda [41]. The chains were found to have converged to the same posterior distribution based 118 on the multivariate version of the Gelman-Rubin diagnostic, which was less than 1.1 for all inferred 119 parameters [42,43]. To ensure maximum robustness, the samples from the four chains were then 120 combined, and found to have an effective sample size of more than 150 for all parameters.

121
Prior distributions of parameters 122 Bayesian inference requires setting plausible priors for the model parameters. We used highly un-123 informative Uniform(0,1) priors for the two proportion parameters and and Uniform(0, 1) priors 124 for the three parameters A sus (0), A res (0) and ↵, which is an improper distribution but does not lead 125 to an improper posterior distribution. For the five remaining parameters ✓, ⌫, , µ and ⇢ we assigned 126 informative Gamma priors based on literature review, as summarised in Table 1. 127 The transmission rate of infection, represented by the parameter ✓, encompasses both the average 128 number of sexual partners per year and the transmission probability per partnership. The Natsal-3 129 survey observed a mean number of sexual partners per year for MSM of 4.4 [34] and we would therefore 130 expect ✓ to be slightly lower, to reflect the fact that not all contacts result in transmission. The prior 131 distribution for ✓ was therefore set such that it was between 2.9 and 6.3 with 99% prior weight. 132 The expected duration of carriage for asymptomatic gonorrhea is not well measured. A study of 18 133 asymptomatic infected men saw no resolution in urethral infection in the 165 days until they received 134 treatment [51]. Estimates of the duration of carriage in modelling studies have been based on calculations 135 that take into account observed prevalence and assumed proportion of unobserved infection, and are 136 often in the region of 6 months [52][53][54]. However, in recent work using genomic data, the greatest 137 observed time to most recent common ancestor for bacterial genomes from known contact pairs was 8 138 months, suggesting that this estimate needs to be increased [30]. Duration of carriage may depend on 139 infection site, for pharyngeal gonorrhea it has been estimated at 12 weeks, and for rectal infection has 140 been estimated at one year [49,55]. The parameter ⌫ was therefore assigned a prior that corresponded 141 to a mean duration of carriage between three months and one year with 99% prior weight. 142 The duration of the incubation, symptomatic and treatment stages of infection have been estimated to 143 be short, in the region of days rather than weeks [45][46][47]. Gamma priors were accordingly assigned to 144 each of the three parameters , µ and ⇢.

145
Estimation of model parameters 147 We fitted our model of gonorrhea transmission to two different time series over the years 2008 to 2014: 148 the total number of gonorrhea diagnoses in MSM in England [44] and the incidence of cefixime-resistant 149 gonorrhea [14][15][16][17][18][19][20]. The posterior distribution of parameters shown in Fig 3 was obtained through 150 Bayesian inference, implemented using a pMCMC method [39]. For each parameter we report the 151 posterior mean estimate and 95% credibility interval shown in brackets (  decreased. 212 We can estimate the frequency of cefixime prescriptions above which we expect the resistant strain 213 to persist, corresponding to when the fitness benefit of cefixime resistance is greater that its fitness 214 cost, by setting R res 0 (⇡) = 1 and solving for ⇡ in Eq 2. We denote this threshold ⇡ If cefixime were used to treat more than 60% of cases then the level of cefixime resistance would 238 become greater than 50% at the end of the eight year simulation period and if cefixime were used to 239 treat all cases resistance would be close to 100%.

241
We have used mathematical modelling and Bayesian inference methods to uncover insights into the 242 dynamics of cefixime resistance in gonorrhea. We quantified both the fitness cost and fitness benefit of 243 resistant strains, which allowed us to make predictions about the future prevalence of resistance as a 244 function of how often cefixime is prescribed. Our results indicate that cefixime could be used to treat 245 uncomplicated cases of gonorrhea without incurring the risk of causing a resistant epidemic like the 246 one that happened in 2007-2012, provided its frequency of use were controlled, enabling continued use 247 of an 'abandoned' antibiotic. Our analysis suggests that cefixime could be used to treat up to 25% of 248 cases, but this threshold should be used cautiously for reasons described below. 249 Our modelling approach requires making a number of assumptions, and it is important to consider their 250 suitability. For example, it was assumed that all cefixime-susceptible infections were cured, regardless 251 of which antibiotic was prescribed. The prescription data shows that between 2008 and 2015 the 252 vast majority of non-cefixime prescriptions were for ceftriaxone, either alone or in combination with 253 azithromycin, so the assumption of cure is reasonable given that ceftriaxone resistance reports remain 254 sporadic in England. Our model implicitly assumes that there is no co-infection with both strains, 255 and no evolution of resistance happening within-host. This simplification has been used in a number 256 other studies on the epidemiology of antimicrobial resistance [61,62]. Ignoring within-host competition 257 between resistant and susceptible strains following co-infection is justified here by the fact that both 258 strains have low prevalence, making co-infection very unlikely. Within-host evolution of resistance was 259 included in a recent gonorrhea modelling study [67] but clearly this is a rare event which only increases 260 by one the number of resistant infections, which we estimated to be already equal to 49 at the start of 261 the epidemic simulation on the 1st January 2008. 262 Our model also makes assumptions concerning the cost and benefit of cefixime resistance. The fitness 263 cost of the mutation conferring resistance is assumed to be constant over time, however compensatory 264 mutations have been observed in other bacterial pathogens that reduce the initially high fitness cost 265 of antibiotic resistance [59,63]. It is clear from our analysis that there was a substantial fitness cost 266 to cefixime resistance when the prescription protocol was changed in 2010, which is the reason why 267 the resistance level subsequently fell. We cannot rule out that compensatory mutations took place 268 after resistance initially emerged, but this would mean that the initial cost was even higher and in these 269 conditions resistance would have been unlikely to emerge at all. Our formulation of the dynamics of the 270 fitness cost of resistance was via a reduction in the duration of asymptomatic carriage. In the absence 271 of evidence of the resistance mechanism the fitness cost could plausibly be modelled through reduced 272 transmissibility of the resistant strain [67], which would not affect our overall conclusions, in particular 273 regarding the basic reproduction number analysis and predictions of the impact of cefixime usage on 274 future resistance trends. 275 The ceftriaxone-azithromycin dual therapy is currently effective, but it represents a last resort, so that we 276 urgently need a strategy for what would be done if it stopped working. It is likely to be just a matter of 277 time before this happens, with the first reported failure of the dual therapy having occurred in 2015 [65]. 278 Resistance to azithromycin was detected in a recent outbreak that started in the North of England [5], 279 and is now reported in almost 10% of tested isolates [21]. Resistance to ceftriaxone remains rare, but 280 minimum inhibitory concentration (MIC) levels have been steadily increasing [66]. If alternative treatment 281 options could be used, even for a minority of cases, then it would delay and maybe even prevent the 282 emergence of resistance to the dual-therapy antibiotics by reducing the fitness benefit it would confer. 283 For some previously used antibiotics, such as penicillin or ciprofloxacin, significant levels of resistance 284 remain in the gonococcal population (24% and 39% in 2015, respectively [21]), so that they could 285 not be recommended even for a small fraction of cases. These antibiotics could be prescribed only if 286 drug-sensitivity could be quickly established, for example using real-time PCR assays [70,71], or whole 287 genome sequencing [64,72], which both remain experimental. In contrast, the fact that resistance to 288 cefixime has become very low in England (around 1% in 2015, [21]) makes it a prime candidate for return 289 into action without the need for case-by-case susceptibility testing. Perhaps the greatest threat posed by 290 this proposed strategy would be the evolution of compensatory mutations which could reduce the fitness 291 cost of resistance. As previously mentioned, compensatory mutations do not seem to have emerged 292 during the 2007-2012 cefixime-resistant epidemic, but if they did the acceptable prescribing proportion 293 would be lowered, and the probability of persistence of cefixime resistance increased. Therefore, a 294 redeployment of cefixime would require close monitoring of resistance trends in England [68] and 295 beyond [73,74].      Table 1. The 301 population was assumed to be closed due to the short time period under consideration. A deterministic 302 version of the model is described by the following differential equations. 303 dS(t) dt = ✓ I(t) N S(t) + ⌫ A sus (t) + ↵A res (t) + ⇢ T sus;cef (t) + T sus;oth (t) + (1 )T res;cef (t) + T res;oth (t) (S1) We used a stochastic version of the model described in Eqs S1-S6. The process was initialised on 31 December 2007, with an initial number of asymptomatic infections A s (0) for s 2 {sus, res}, and the remainder of the population being susceptible. Simulation proceeds through repeated iterations of the steps below for each day. Transition variables d 1 , ..., d 17 are drawn from the following distributions: , ⇡(t)µ, (1 ⇡(t))µ (S10) d 9 , d 10 ⇠ Multinom E res (t), ⇡(t)µ, (1 ⇡(t))µ (S11)