Can phenotypic data complement our understanding of antimycobacterial effects for drug combinations?

Investigating Combination Therapy as a Means to Enhance Activity and Repurpose Antimicrobials

Current clinical practice assumes that a single antibiotic given as a bolus or as a course will successfully treat most infections. In modern medicine, this is becoming less and less true with drug-resistant, multi-drug-resistant, extensively drug-resistant, and untreatable infections becoming more common. Where single-drug therapy (monotherapy) fails, we will turn to multi-drug therapy. Alternatively, combination therapy could be useful to prevent the emergence of resistance. Multi-drug therapy is already standard for some multi-drug resistant infections and is the standard for the treatment of some pathogens such as Mycobacterium tuberculosis.The use of combination therapy for everyday infections could be a clear course out of the current AMR crisis we are facing. With every additional drug added to a combination (n + 1) the likelihood of the pathogen evolving resistance drops exponentially.Many generic antibiotics are cheap to manufacture as they have fallen out of patent protection but are less effective at pharmacologically effective doses due to overuse in the past. Combination therapy can combine these generic compounds into cocktails that can not only treat susceptible and resistant infections but can also reduce the risk of new resistances arising and can resuscitate the use of antimicrobials once thought defunct.In this chapter, we will summarize theory behind combination therapy and standard in vitro methodologies used.

Using Hollow Fiber to Model Treatment of Antimicrobial-Resistant Organisms

The use of animal models is still widespread in science but there is a movement away from this manner of experimentation. One option approved by the FDA for human-like studies is the hollow fiber bioreactor (HFS). HFSs are highly controllable, self-contained systems that allow for the modeling of individual tissues and disease phenotypes. Oxygen, drug concentration & half-life, and immune cell invasion are all scalable to human and veterinary conditions using a HFS. There are drawbacks to the systems including cost and contamination so the use of these systems must be carefully managed.With these limitations in mind, the scope of the technology is great. Antimicrobial susceptibility testing (AST) is possible with greater accuracy and clinical validity than classical in vitro techniques making minimal inhibitory concentration (MIC) data generated on the bench more translatable to the clinic.In this chapter, we will outline the background of the HFS and some typical uses.

Improving the Drug Development Pipeline for Mycobacteria: Modelling Antibiotic Exposure in the Hollow Fibre Infection Model

Mycobacterial infections are difficult to treat, requiring a combination of drugs and lengthy treatment times, thereby presenting a substantial burden to both the patient and health services worldwide. The limited treatment options available are under threat due to the emergence of antibiotic resistance in the pathogen, hence necessitating the development of new treatment regimens. Drug development processes are lengthy, resource intensive, and high-risk, which have contributed to market failure as demonstrated by pharmaceutical companies limiting their antimicrobial drug discovery programmes. Pre-clinical protocols evaluating treatment regimens that can mimic in vivo PK/PD attributes can underpin the drug development process. The hollow fibre infection model (HFIM) allows for the pathogen to be exposed to a single or a combination of agents at concentrations achieved in vivo-in plasma or at infection sites. Samples taken from the HFIM, depending on the analyses performed, provide information on the rate of bacterial killing and the emergence of resistance. Thereby, the HFIM is an effective means to investigate the efficacy of a drug combination. Although applicable to a wide variety of infections, the complexity of anti-mycobacterial drug discovery makes the information available from the HFIM invaluable as explored in this review.

Evaluation of pharmacokinetic-pharmacodynamic relationships and selection of drug combinations for tuberculosis

AIMS

Despite evidence of the efficacy of anti-tubercular drug regimens in clinical practice, the rationale underpinning the selection of doses and companion drugs for combination therapy remains empirical. Novel methods are needed to optimise the antibacterial activity in combination therapies. A drug-disease modelling framework for rational selection of dose and drug combinations in tuberculosis is presented here.

METHODS

A model-based meta-analysis was performed to assess the antibacterial activity of different combinations in infected mice. Data retrieved from the published literature were analysed using a two-state bacterial growth dynamics model, including fast- and slow-growing bacterial populations. The contribution of each drug to the overall antibacterial activity of the combination was parameterised as relative change to the potency of the backbone drug (EC₅₀ -F and/or EC₅₀ -S). Rifampicin and bedaquiline were selected as paradigm drugs to evaluate the predictive performance of the modelling approach.

RESULTS

Pyrazinamide increased the potency (EC₅₀ -F and EC₅₀ -S) of rifampicin (RZ) and bedaquiline (BZ) by almost two-fold. By contrast, pretomanid and isoniazid were found to worsen the antibacterial activity of BZ and RZ, respectively. Following extrapolation of in vivo pharmacokinetic-pharmacodynamic relationships, the dose of rifampicin showing maximum bactericidal effect in tuberculosis patients was predicted to be 70 mg·kg^-1 when given in combination with pyrazinamide.

CONCLUSIONS

The use of a drug-disease modelling framework may provide a more robust rationale for extrapolation and selection of dose and companion drugs in humans. Our analysis demonstrates that RZ and BZ should be considered as a backbone therapy in prospective novel combination regimens against tuberculosis.