The uncertain role of substandard and falsified medicines in the emergence and spread of antimicrobial resistance

Abstract

Approximately 10% of antimicrobials used by humans in low- and middle-income countries are estimated to be substandard or falsified. In addition to their negative impact on morbidity and mortality, they may also be important drivers of antimicrobial resistance. Despite such concerns, our understanding of this relationship remains rudimentary. Substandard and falsified medicines have the potential to either increase or decrease levels of resistance, and here we discuss a range of mechanisms that could drive these changes. Understanding these effects and their relative importance will require an improved understanding of how different drug exposures affect the emergence and spread of resistance and of how the percentage of active pharmaceutical ingredients in substandard and falsified medicines is temporally and spatially distributed.

Fig. 1. The potential impact of antibiotic dose on the rate of resistance emergence.

Based on Kouyos et al.. The rate of resistance emergence is minimized at high doses (when both sensitive and resistant pathogens are eliminated) and low doses (when there is no selective pressure in favor of resistance), and is maximized in between. Closed circles indicate example rates of resistance emergence for different percentages of active pharmaceutical ingredients (API). Open circles represent the rate of resistance emergence with the standard course. The first row indicates a situation where the standard course has a percentage API to the right of the peak in the rate of resistance of emergence, and the second row when the percentage API is to the left of the peak. The difference in the rate of resistance for the closed compared to open circles indicates whether SF medicines will increase or decrease the overall rate of resistance emergence.

Fig. 2. Summary of mechanisms by which substandard and falsified antimicrobials (SF) could affect the emergence of AMR.

The first column shows the baseline scenario, and the second and third columns show slightly reduced and much reduced percentages of active pharmaceutical ingredient (API) respectively. For simplicity, we do not show situations with a low baseline percentage API or when API is increased in substandard or falsified antimicrobials. The symbols in the column headers indicate the percentage API and the effect on the rate of resistance emergence (i.e., the inverse of the average time for a resistant pathogen to become established). Solid lines indicate transitions in the same individual, and dashed lines indicate transmission. A The effect on the rate of de novo emergence of resistance. B The effect on the density of resistant organisms. C SF antimicrobials will prolong the infectious periGod, leading to more opportunities for transmission. D By reducing the efficacy of treatment, SF antimicrobials could lead to fewer susceptible hosts. E Similarly, they could iFncrease transmission from those with sensitive organisms, indirectly reducing transmission from those with resistant organisms. The nature of this effect will also depend on effect A (de novo emergence); if SF medicines increase establishment of resistance, then they could instead increase the proportion of transmission from individuals with resistant infections. F Here T refers to the target microbe, and B to a bystander, while the subscript indicates whether they are resistant or sensitive to the antimicrobial. SF antimicrobials could potentially affect bystander selection in the same way they reduce de novo emergence in the target medicine, though the levels of API which lead to the highest rate of resistance emergence will likely differ to that of the target pathogen. G Here the subscript refers to the antimicrobial (X or Y) to which they are sensitive (S) or resistant (R). In this example, the pathogen is fully resistant to one of the medicines in the combination, and sensitive to the other.

Fig. 3. Medicines with no active pharmaceutical ingredient (API) can contribute to the emergence of resistance when followed with a standard treatment regimen.

Black lines represent a standard regimen with high-quality antimicrobial taken at time 0, while Red lines represent a standard regimen delayed by 3 days - e.g., because the initial regimen contained no API. A: Example concentration-time curves from a pharmacokinetic model with exponential absorption and exponential elimination. Concentration for a single dose at time 0 is then given by $\mathrm{C}\left(\mathrm{t}\right)=\frac{\mathrm{FD}{\mathrm{k}}_{\mathrm{a}}}{\mathrm{V}\left({\mathrm{k}}_{\mathrm{a}}-\mathrm{k}\right)}\left({\mathrm{e}}^{-\mathrm{kt}}-{\mathrm{e}}^{-{\mathrm{k}}_{\mathrm{a}}\mathrm{t}}\right)$, where F is the bioavailability, here assumed to be 1, V = 931 L is the volume of distribution, D = 2000 mg is the dose, k_a = 0.154 hr⁻¹ is the absorption rate, and k = 2.00×10⁻³hr⁻¹ is the elimination rate. The regimen consists of once daily treatment taken for 3 days. B: Example pathogen abundance curves. Pathogen abundance was described by logistic growth and exponential decay (both drug-induced and natural), i.e., $\frac{\mathrm{dB}}{\mathrm{dt}}=\mathrm{gB}\left(1-\frac{\mathrm{B}}{{\mathrm{B}}_{\max }}\right)-\mathrm{\alpha }\mathrm{B}$, where, g = 0.6 hr⁻¹ is the growth rate, B_max = 1×10⁶ is the carrying capacity, and α is the death rate. Pharmacodynamics are described by a sigmoid relationship between the drug-induced death rate and concentration, i.e., $\mathrm{\alpha }={\mathrm{\alpha }}_0+\frac{\left(\mathrm{\kappa }-1\right)\mathrm{\alpha }\mathrm{C}}{\mathrm{C}+{\mathrm{C}}_{50}}$, where, α₀ = 0.3 hr⁻¹ is the natural death rate, κ = 5 is the maximum proportional increase in death rate, and C₅₀ = 10 mg l⁻¹ is the concentration at which the death rate is at half its maximum value. C: Example resistant infection potential (RIP) curves. The area under these curves is proportional to the potential for onward transmission of acquired resistance, and is higher with delayed treatment. The quantity is based on Grenfell et al., and is given by $\mathrm{RIP}\left(\mathrm{t}\right)\propto \mathrm{B}\left(\mathrm{t}\right){\int }_0^{\mathrm{t}}\mathrm{mB}\left(\mathrm{t}\right)\mathrm{C}\left(\mathrm{t}\right)$, where m is the mutation rate. We assume that the proportion of resistant mutants that become fixed is proportional to C, but the qualitative pattern would also hold provided that this proportion was a monotonic function of C.

Box Fig. 1 Substandard and falsified (SF) medicines can undermine artemisinin-combination therapies (ACTs) in several ways. A Under normal circumstances, the ACT at the correct dose should lead to clearance of parasites. B An SF artemisinin-derivative with too little active pharmaceutical ingredient (API) could lead to hyperparasitaemia. C If either (or both) of the APIs in the ACT are SF, there is an increased risk of recrudescence. D. If the partner drug is SF, then the artemisinin-derivative could be left unprotected due to the partner drug’s reduced concentration. Upper panel based on Fig. 1 in White et al..