Chapter 7: Conclusions
7.5 Implication of findings on tuberculosis and HIV treatment and research
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156 being their own control is great, there is a potential of lingering pregnancy changes that can influence the drug's pharmacokinetics and thus impair the comparison. For this reason, if pregnant women are recruited within a broader trial, this may provide an ideal control group of patients recruited in the same centres that underwent the same study procedures and received the same drug formulations.
In addition, clinical trials involving breastfeeding mothers should sample breastmilk to quantify the amount of drug the infant is ingesting, taking into account that the drug’s pharmacokinetics might difference between infants and adults, and assessing the potential risk and benefits of the drug exposure in the infant. Physiological based Pharmacokinetics (PBPK) modelling is another area of interest utilised in describing drug exposures, especially when clinical pharmacokinetics data is lacking, which is currently the case for pregnant and breastfeeding women. PBPK modelling could be used to describe the potential impact of physiological and anatomy changes during pregnancy on the drug's pharmacokinetics and also the drug exposure in breastmilk (Metushi et al., 2016). Therefore, PBPK is a predictive tool that can be used to assess the benefit and risk to inform decision making for dose adjustment or prohibition of such medication in pregnant or breastfeeding women to ensure the safety of both the mother and the baby.
7.5.2 Pharmacogenetic testing and therapeutical drug monitoring
Interindividual variability to a standard dose of a regimen is a crucial problem in clinical practice, as it could lead to non-responsive or adverse drug reactions. A significant portion of the variability in drug response can be attributed to genetic factors which could potentially influence the drug pharmacokinetics and/or pharmacodynamics (Gervasini et al., 2010). If available, pharmacogenetic information can be used to identify individuals at risk of
157 subtherapeutic or toxic exposures, hence increasing the number of responders while reducing the occurrence of adverse events. The FDA encourage the capturing of genetic information to aid in optimising dose, which has led to the modification of some drug labs to include pharmacogenetic information (Drozda et al., 2018; Flockhart et al., 2009). The regulatory agencies play a big role in establishing clinically and commercially robust pharmacogenetic testing by developing guidelines specifying drugs for which predictive genotyping should be considered before initiating treatment therapy (Gervasini et al., 2010).
Therapeutical drug monitoring (TDM) is another useful tool to check the exposure in patients and individualise the therapy accordingly. While TDM can be used to identify the patient’s phenotype (e.g., fast vs. slow metaboliser), the observed concentrations will also reflect nongenetic factors that influence (DDIs, adherence, organ function and disease) the exposure of a drug. The disadvantage of therapeutical drug monitoring over pharmacogenetic testing is that the drug quantifying procedure is time-consuming, expensive, and laborious. The usefulness of pharmacogenetic testing depends on the availability of well-established information about the influence of genetic factors on drug exposure, but once that is available, genetic testing is becoming less expensive and more widely available.
Drugs that are heavily influenced by polymorphic metabolic pathways like isoniazid and efavirenz would greatly benefit from pharmacogenetic testing. Two common isoniazid related adverse events are peripheral neuropathy and hepatitis (Werely et al., 2007).
Peripheral neuropathy is associated with higher plasma isoniazid concentration and slow NAT2 acetylators. However, the drug-induced peripheral neuropathy is preventable by supplementation of pyridoxine, which is currently recommended for all isoniazid therapy (LOTTE et al., 1964). The risk of isoniazid-induced hepatitis is higher in slow acetylators, who tend to have higher concentrations of isoniazid and its metabolite acetylhydrazine for longer
158 periods than rapid acetylators, as both parent and metabolite have been linked to liver injury (Metushi et al., 2016). Isoniazid efficacy is also linked to NAT2 genotype since the exposures significantly differ between the three phenotypes. Hence fast and intermediate acetylators have lower bactericidal activity than slow acetylators administered similar isoniazid doses (Donald et al., 2007). Central nervous system (CNS) complication is the most common efavirenz adverse event. CNS toxicity is related to efavirenz plasma levels and has been linked to CYP2B6 genotype (Rotger et al., 2005). Isoniazid pharmacogenetic marker NAT2 already has a test kit, while for efavirenz, TDM assay has been utilised for drug efficacy and to avoid toxicity (Gervasini et al., 2010). Recently a prototype automated pharmacogenomics assay on the Genexpert platform, which is widely available globally but not yet applied to pharmacogenomics, has been developed to robustly identify NAT2 acetylator using a volume of 24 µL blood sample (Verma et al., 2021). The prototype is easy to implement, scalable hence could be used at point of care, and requires minimal hands-on time for sample preparation, facilitating its use in resource-constrained settings like South Africa.
7.5.3 Handling of sparse or noisy data
The thesis emphasizes how nonlinear mixed-effects modelling is a powerful tool for efficiently analysing clinical trial data especially sparse and noisy data. The bedaquiline pharmacokinetics data analysed in chapter 4 had a small sample size and was sparse;
however, with the help of a pharmacokinetic model, we could extract the information presented in that chapter. Furthermore, PK/PD models can be used to predict and extrapolate through simulations of validated models and have better capabilities of handling both sparse and noisy data. The PK/PD model developed in chapter 6 can be used to optimise other
159 tuberculosis drugs. While the consideration like the use of joint models can be utilised when analysing noisy longitudinal data that have correlating biomarkers or several biomarkers that need to be considered at once, as the models have better capabilities of extracting signal from noise.