Metabolomics describes previously unknown toxicity mechanisms of isoniazid and rifampicin

Journal Pre-proof Metabolomics describes previously unknown toxicity mechanisms of isoniazid and rifampicin Monique Combrink, Du Toit Loots, Ilse du Preez

PII:

S0378-4274(20)30026-6

DOI:

https://doi.org/10.1016/j.toxlet.2020.01.018

Reference:

TOXLET 10687

To appear in:

Toxicology Letters

Received Date:

6 November 2019

Revised Date:

17 January 2020

Accepted Date:

21 January 2020

Please cite this article as: Combrink M, Loots DT, du Preez I, Metabolomics describes previously unknown toxicity mechanisms of isoniazid and rifampicin, Toxicology Letters (2020), doi: https://doi.org/10.1016/j.toxlet.2020.01.018

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Title page

Metabolomics describes previously unknown toxicity mechanisms of isoniazid and rifampicin Monique Combrink1, Du Toit Loots1, Ilse du Preez1* 1

Human Metabolomics, North-West University (Potchefstroom Campus), Private Bag x6001, Box 269,

Potchefstroom, South Africa, 2531.

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*Corresponding author: [email protected]

Highlights

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Isoniazid and rifampicin are the most important first-line TB drugs Despite the drugs’ anti-mycobacterial properties, it can cause severe side-effects. Drug toxicity is a major contributor to TB treatment failure. Metabolomics can be used to elucidate TB drug toxicity mechanisms. Metabolomics can contribute to the development of less toxic TB drugs.

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    

Abstract

Isoniazid and rifampicin are well-known anti-mycobacterial agents and are widely used to treat

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pulmonary tuberculosis (TB) as part of the combined therapy approach, recommended by the World Health Organization. The ingestion of these first-line TB drugs are, however, not free of side-effects, and are toxic to the liver, kidney, and central nervous system. These side

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effects are associated with poor treatment compliance, resulting in TB treatment failure, relapse and drug resistant TB. This occurrence has subsequently led to the recent application

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of novel research technologies, towards a better understanding of the underlying toxicity mechanisms of TB drugs in humans, mostly focussing on the 2 most important TB drugs: isoniazid and rifampicin. In this review, we discuss the contribution that one such an approach, termed metabolomics has made toward this field, and also highlight the impact that this might have towards the development of improved TB treatment regimens.

Abbreviations

Central nervous system

Cr/PCr

Creatine-phosphocreatine

EMB

Ethambutol

GABA

Gamma-aminobutyric acid

HD

High dose

INH

Isoniazid

INH-GLC

β-glucosyl isonicotinylhydrazide

INH-PA

Pyruvate isonicotinylhydrazone

LD

Low dose

NAM

Nicotinamide

PPIX

Protoporphyrin IX

PZA

Pyrazinamide

RIF

Rifampicin

TB

Tuberculosis

TCA

Tricarboxylic acid cycle

TMAO

Trimethylamine N-oxide

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CNS

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UDP-GalNAc Uridine diphosphate N-acetylglucosamine

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1. Introduction

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Keywords: Isoniazid, metabolomics, rifampicin, toxicity, tuberculosis.

The currently recommended treatment regimen for pulmonary TB, involves the coadministration of four drugs (isoniazid, rifampicin, ethambutol and pyrazinamide) for two-

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months, followed by a four-month maintenance phase requiring the administration of only isoniazid and rifampicin (WHO, 2019). When this treatment regime is fully adhered to, it has a high cure rate of 85 %. Nevertheless, according to the latest treatment outcome data, a significant fraction (15 %) of TB cases are still being treated unsuccessfully (WHO, 2019). This occurrence can be ascribed to several factors, amongst others, inter-individual metabolic variation and the disruption and/or premature termination of the first-line treatment approach, the latter of which is ascribed to the rather severe side-effects resulting from the ingestion of these drugs (De Villiers and Loots, 2013; WHO, 2019).

A better understanding of the

underlying toxicity mechanisms associated with these drugs, and the subsequent development of more effective, less-toxic TB therapy approaches, could play a significant role in lowering the incidence of TB treatment failure. The application of newer technologies and novel research approaches, such as metabolomics has become increasingly popular amongst the TB research community, as previously reviewed by our group (Du Preez and Loots, 2018; Du Preez et al., 2017; du Preez et al., 2019; Luies et al., 2017). Metabolomics can be defined as an unbiased research approach ideally suited for the identification and quantification of all metabolites present in a biological sample, via use of highly selective and sensitive analytical methods, in conjunction with bioinformatics (Dunn and Ellis, 2005) (see Figure 1). The aim of metabolomics is, therefore,

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to comprehensively analyse and measure the metabolome (collection of the endogenous metabolites) and the alterations thereof, in response to a specific perturbation (Robertson et al., 2011).

If the perturbation being investigated is an ingested drug for example, the

abundance of various normally occurring endogenous metabolites (small molecular compounds produced from the cellular metabolism) may be altered, reflecting variations in

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their representative pathways, in addition to the novel occurrence of specific drug derived

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metabolites (Roux et al., 2011).

Figure 1: Example of the application of metabolomics for drug toxicity research. (a) Biological samples (serum, plasma, or urine) are collected from a cohort of individuals undergoing treatment and grouped according to the presence or absence of side-effects.

(b) The

metabolome or subsets of the metabolome (such as the organic acids or fatty acids) are then extracted from the collected samples using a variety of pre-analytical methods, where after the (c) analytical detection and/or identification of all compounds present in the extract is done

using GC-MS, LC-MS, or NMR systems. (d) The generated data is then subjected to various multivariate and univariate statistical analyses in order to identify significant variations and/or potential biomarkers characterising the cohort groups. (e) Intergroup metabolome variations are then interpreted in the light of known metabolic pathways in an attempt to elucidate underlying mechanisms leading to the observed side effects. GC-MS, gas chromatography– mass spectrometry; LC-MS, liquid chromatography–mass spectrometry; NMR, nuclear

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magnetic resonance; PC, principle component.

2. Metabolomics of isoniazid-induced toxicity

Isoniazid (INH), a bactericidal drug first discovered in 1952, can efficiently and specifically inhibit actively growing M. tuberculosis, and therefore forms an integrated part of the combined DOTS therapy regimen (Zhang, 2004). The use of this drug does, however, have toxic effects to the patient, which can range from less severe skin reactions, to rather severe hepatotoxicity, peripheral neuropathy and central nervous system (CNS) toxicity (Feng et al., 2011). Although various xenobiotic metabolites (drug specific mechanisms), and genetic and environmental host susceptibility factors have been identified to contribute to these adverse effects, the exact underlying mechanisms of INH-induced liver, kidney, CSN injury still remains unclear

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(Boelsterli and Lee, 2014).

2.1 Hepatotoxicity

When applying an untargeted metabolomics approach for the identification of drug-induced

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changes to the liver metabolomes of mice, Sachar et al. (2016) found that chronic INH treatment leads to hepatic accumulation of the hepatotoxin, protoporphyrin IX (PPIX) (Table

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1). In addition, they found that INH downregulates ferrochelatase (FECH), the enzyme that converts PPIX to heme, and, furthermore, the drug also induces delta-aminolevulinate

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synthase 1 (ALAS1), the rate-limiting enzyme in heme biosynthesis. This newly identified knowledge clarifies the mechanism of INH-induced hepatic porphyria and provides an understanding of the contraindication of INH in patients with porphyrias. This discovery also

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sheds light on the fact that the mentioned hepatic porphyria is potentiated in the case of INH and RIF co-treatment (as discussed in Section 4).

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To date it is believed, that hydrazine, a powerful reducing agent and primary metabolite of INH in the host, is the main cause of INH-induced hepatotoxicity. This metabolite has been associated with the formation of megamitochondria in rat livers and ATP depletion in

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hepatocytes (Boelsterli and Lee, 2014).

In 1992, Sanins et al. (1992) applied 1H NMR

technology to investigate the disposition of hydrazine and its effects on various endogenous metabolites in the plasma and urine of rats. Various hydrazine metabolites (acetylhydrazine and diacetylhydrazine) were detected, in addition to a dose-dependent increase in urinary taurine, α-alanine, β-alanine and methylamine, accompanied by a decrease in urinary 2oxoglutarate and an increase in urinary and plasma lactate (Table 1), supporting previous observations of INH-induced mitochondrial dysfunction.

Using a similar

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H NMR

metabolomics approach, Nicholls et al. (2001) identified a number of dose dependent changes

(Table 1) to the urinary and plasma metabolomes of rats after oral hydrazine administration, which correlated with the severity of the hydrazine-induced liver lesions. Their results furthermore supported previous findings related to hydrazine hepatotoxicity mechanisms, including the hydrazine-induced inhibition of 2-aminoadipate aminotransferase, the inhibition of the mitochondrial urea cycle, and reduced liver function. Furthermore, elevated urinary and plasma levels of 2-aminoadipate, also suggested a possible mechanism for the neurological side-effects typically accompanying hydrazine/INH administration. In a follow up study, the same group investigated the interspecies variation of hydrazine toxicity, by comparing the drug-induced metabolome variations in rats and mice.

Elevated urinary h-alanine, 3-d-

hydroxybutyrate, citrulline, N-acetylcitrulline, and reduced trimethylamine-N-oxide, were

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uniquely detected in the rat samples, indicating a higher degree of toxic response in this species than that observed in mice. Similarly however, both species depleted tricarboxylic acid cycle (TCA) intermediates accompanied by an increase in lactate, indicating mitochondrial dysfunction (Bollard et al., 2005). Also aiming to determine the mechanisms related to hydrazine-induced hepatotoxicity, Bando et al. (2011) applied a GC-MS based

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metabolomics approach to investigate the plasma and urine metabolite changes in rats induced by oral hydrazine administration (120 and 240 mg/kg). Various metabolite markers

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associated with the histopathological changes, including those associated with fatty acid degradation and glycogen accumulation, were reported (Table 1).

Furthermore, a dose

dependent increase in the various amino acid precursors of glutathione (cysteine, glutamate

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and glycine), 5-oxoproline (a product of glutathione metabolism), and ascorbate, was identified. The TCA cycle intermediates were detected in reduced concentrations in the hydrazine treated rats, whereas urea cycle metabolites and other amino acids, were found to

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be elevated in this group comparatively. Collectively, the metabolic changes identified in the latter study indicated the importance of oxidative stress and antioxidant consumption in the etiology of hydrazine-induced hepatotoxicity, something worth investigating further for its

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application to pharmacology and toxicology screening.

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2.2 CNS toxicity

Using data generated from 1H NMR metabolomics in combination with various clinical assays, histopathological inspection, and western blots, Ruan et al. (2018) were able to further elaborate on not only previously unknown INH-induced hepatotoxicity, but also neurotoxicity mechanisms.

Post treatment metabolome profiles of rat serum, liver and brain tissue,

indicated that INH induces neurotoxicity in a dose-dependent fashion by inducing 1. a defective neurotransmission (portrayed by significantly reduced levels of glutamate and elevated gamma-aminobutyric acid (GABA) in the treated rats), and 2. neuronal damage via

the induction of osmotic stress (revealed by the reduced myo-inositol and N-acetyl aspartate levels). Furthermore, the substantial reduction in various antioxidants and an upregulated glutathione synthesis pathway were observed and indicative of elevated oxidative stress levels, which, in turn, could be linked to impaired mitochondrial and cell membranes. This was furthermore confirmed by INH induced disruption of the energy metabolism (depletion of NAD+ and significantly reduced ATP), accompanied by the activation of possible alternative ATP generating pathways.

Although it is of utmost importance to understand the general mechanisms and metabolic changes which occur when TB medication is taken by a large patient population, individual

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variation to this treatment response should also be considered especially in the light of poor treatment compliance by some individuals, and subsequently the development of drug resistant strains of M. tuberculosis. To this end, Cunningham et al. (2012) applied a NMRbased metabolomics research approach in order to investigate the variability that exists between individuals developing adverse CNS effects in response to INH treatment, and those

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that don’t (non-responders). Urine was collected from rats receiving the vehicle solution alone (0.9 % saline), or the vehicle solution containing 200 mg/kg or 400 mg/kg INH, respectively,

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and based on the clinical observations, these cohorts were classified as either CNS responders or non-responders. In comparison to non-responders, the INH metabolites INHPA and INH-GLC were detected in significantly elevated concentrations, and AcINH was

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significantly reduced, in the urine of responders (Table 1). These results suggested a reduced capacity for acetylation of INH in the responders, and subsequently, an elevated capacity for the conjugation of INH with pyruvate and glucose.

Additionally, a pre-dose metabolic

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signature, which predicts an individual’s acetylation capacity, and therefore also the likelihood for developing INH related CNS contraindications, before treatment onset, was also identified (Cunningham et al., 2012). This is an important discovery, since it highlights the potential

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clinical contribution that metabolomics research can make towards personalized TB therapy.

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2.3 Multi-organ toxicity

Using MAS NMR metabolomics, Garrod et al. (2005) identified multi-organ (liver, kidney and brain) metabolome variations (Table 1) after a single dose of hydrazine (90 mg/kg) in rats. A hydrazine-induced increase in alanine in all three organ tissue samples analysed, confirming the previously known inhibitory effect of hydrazine on aminotransferase, and the consequent effect on GABA aminotransferase, confirmed by elevations in brain GABA levels, were observed.

They additionally discussed the possibility that the reduction in hepatic

glucose/glycogen oligomers detected, may be due to defective glycogen storage or release in

the liver, and/or a reduced apolipoprotein complex production, which is responsible for triglyceride transport out of the liver. The authors furthermore proposed that the observed reduction in hepatic and renal taurine, may be due to hydrazine causing either: 1. altered membrane permeability of the hepatocytes; 2. interference with bile salts or bile production; 3. increased synthesis of taurine in response to the toxic challenge; or 4. inhibition of renal taurine uptake (directly or via competitive uptake inhibition by β-alanine). The aforementioned hydrazine-induced elevations to hepatic β-alanine, was attributed to an increased pyrimidine catabolism, and the observed reduction of myo-inositol, choline metabolism associated metabolites, and N-acetyl aspartate in the brain, were thought to be associated with a disruption in ion regulation and/or membrane structure. These metabolomics results also

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prove the capacity for such a research approach to better elucidate drug induced site-specific changes to the metabolome, in order to better understand the mechanisms of drug toxicity. 2.4 Acute isoniazid overdose

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In addition to the toxic effects of chronic treatment at the recommended dose (~5mg/kg/d), intentional or accidental overdose of INH (≥30 mg/kg/d) can lead to seizures, metabolic

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acidosis, coma or even death, due to its acute neurotoxicity (≥80 mg/kg/d) (Khoharo et al., 2009). Using an untargeted pharmacometabolomics approach, Li et al. (2016) investigated the altered liver biochemical pathways induced by acute INH poisoning in mice (Table 1).

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Elevated levels of hepatic heme, oxidized NAD, oleoyl-L-carnitine and linoleoyl-L-carnitine were reported, indicative of mitochondrial dysfunction. Interestingly, INH-fatty acid amides were also identified in the INH poisoned animals, indicating previously unidentified interactions

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between INH and fatty acyl coenzyme A’s (CoAs). Furthermore, a novel INH - NAD adduct, in which the nicotinamide moiety of NAD was replaced by INH, was identified in the mice receiving the drug. Lastly, the accumulation of cystathionine in this group, suggests that an

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INH overdose blocks vitamin B6-dependant cystathionine degradation, thereby depleting hepatic vitamin B. These findings prove that, in addition to the aforementioned neurotoxicity,

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acute INH poisoning also results in a disruption in various metabolic pathways of the liver, leading to severe hepatotoxicity.

Metabolites with increased abundance after drug exposure

Isoniazid and Hydrazine-induced toxicity Urine Taurine, α-alanine, β-alanine, methylamine, lactate Rats Plasma Lactate

Rats

Urine

Mice

Urine

Rats

Urine (LD,24h) Urine (LD,48h)

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Urine (HD,24h) Urine (HD,48h) Plasma (LD,24h)

Plasma (LD,48h) Plasma (HD,24h)

Rats

Plasma (HD,48h) Liver

Toxicity investigated

Reference

2-Oxoglutarate -

Hepatotoxicity

Sanins et al. (1992)

Hippurate, citrate, succinate, 2-oxoglutarate, TMAO, fumarate, creatinine

Hepatotoxicity

Nicholls (2001)

trimethylamine-N-oxide,

Hepatotoxicity

Bollard et al. (2005)

Glutamate, fumarate, pipecolate, citrate, succinate, oxaloacetate, 2-oxoglutarate

Hepatotoxicity

Bando et al. (2011)

Multi-organ toxicity

Garrod et al. (2005)

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-

2-Oxoglutarate, hippurate

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Plasma

Taurine, creatine, threonine, N-methylnicotinic acid, βalanine, lactate, 2-aminoadipic acid, citruline, Nacetyl-citruline, argininosuccinate Lypoproteins, glycine, alanine, 3-D-hydroxybutyrate, creatine, valine, isoleucine, histidine, threonine, tyrosine, citrulline, 2-aminoadipic acid, lysine, arginine Acetyl hydrazine, diacetyl hydrazine, 1,4,5,6tetrahydro-6-oxo-3-pyridazine carboxylic acid (THOPC), β-alanine, 2-aminoadipic acid, 3-d-hydroxybutyrate, citrate, citrulline, N-acetyl-citrulline, creatine, creatinine, α-glucose, β-glucose, hypotaurine, lactate, taurine Diacetyl hydrazine, THOPC, 2-aminoadipic acid, citrate*, creatine creatinine, guanidinoacetic acid, hippurate**, lactate, succinate*** Cysteine, ascorbate, putrecine, glycine, GABA, citrulline, lysine, cadaverine, 2-aminoadipate, glucose, malate Cysteine, glutamate, ascorbate, putrecine, glycine, GABA, lysine, cadaverine, 2-aminoadipate, pipecolate, glucose, succinate, oxaloacetate Cysteine, ascorbate, putrecine, glycine, GABA, lysine, cadaverine, 2-aminoadipate, glucose, oxaloacetate Cysteine, ascorbate, putrecine, glycine, GABA, citrulline, lysine, cadaverine, 2-aminoadipate, pipecolate, glucose, malate 5-Oxoproline, cysteine, glutamate, putrecine, urea, GABA, ornithine, fumarate, 2-aminoadipate, pyruvate, lactate, malate 5-Oxoproline, cysteine, glutamate, putrecine, urea, ornithine, fumarate, 2-aminoadipate 5-Oxoproline, cysteine, putrecine, glycine, urea, GABA, ornithine, fumarate, 2-aminoadipate, pyruvate, lactate, malate 5-Oxoproline, cysteine, putrecine, urea, ornithine, fumarate, 2-aminoadipate Alanine, β-alanine, triglycerides

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Urine

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Rats

Metabolites with decreased abundance after drug exposure

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Sample matrix

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Research model

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Table 1: Metabolomics studies investigating isoniazid- and rifampicin-induced toxicity

succinate,

Trimethylamine

Citrulline, fumarate, citrate, malate, 2-oxoglutarate

Glutamate, citrulline, fumarate, pipecolate, citrate, succinate, malate, 2-oxoglutarate Glutamate, fumarate, citrate, succinate, oxaloacetate, 2-oxoglutarate Glycine, lysine, glucose, citrate

Glycine, GABA, lysine, glucose, pyruvate, citrate, lactate, malate Glutamate, lysine, glucose, citrate

Glutamate, glycine, GABA, lysine, glucose, pyruvate, citrate, lactate, malate Choline, -glucose, β-glucose, glycogen, taurine, trimethylamine-N-oxide

Kidney

Alanine, β-alanine, 2-aminoadipate, tyrosine

Rats

Brain Urine

Alanine, aspartate, creatine, GABA INH-PA, INH-GLC, lactate, glucose

Mice Mice

Liver Liver

Rats

Serum

Protoporphyrin IX Oleoyl-L-carnitine, linoleoyl-L-carnitine, INH-fatty acid amides, heme, NAD, INH-NAD, cystathionine 2-Hydroxybutyrate, valine, leucine, 2-aminobutyrate, 2hydroxyisovalerate, lactate, Cr/PCr, tyrosine, ornithine, glycine, glycogen

Liver

Lactate, 2-aminoadipate, acetate, ornithine, taurine, glycine, β-alanine, fumarate, tyrosine

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Leucine, alanine, GABA, acetate, tyrosine, NAD +

3-Hydroxybutyrate, lysine, acetoacetate, succinate, glutamate, citrate, aspartate, methionine, N,Ndimethylglycine, histidine, TMAO, threonine, glycerol, glycerate, phenylalanine, maleic acid Valine, isoleucine, 3-hydroxybutyrate, threonine, glutamate, glutamine, succinate, N-acetyl aspartate, glutathione, choline, phosphocholine, taurine, betaine, Cr/PCr, myo-Inositol, ascorbate, phenylalanine, inosine, NAM, fumarate, β-glucose Valine, isoleucine, 3-hydroxybutyrate, alanine, glutamate, glutamine, glutathione, Cr/PCr, choline, phosphcholine, TMAO, UDP-GalNAc, uridine, inosine, nicotinamide ribotide, hydroquinone, histidine, phenylalanine, NAM, adenine, formic acid, ATP, NAPD+, NAD+

CNS toxicity Hepatotoxicity Neurotoxicity and hepatotoxicity Neurotoxicity hepatotoxicity

severe and

Cunningham et al. (2012) Sachar et al. (2016) Li et al. (2016) Ruan et al. (2018)

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Brain

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myo-inositol, taurine,

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Choline, glutamate, lysine, trimethylamine-N-oxide myo-inositol AcINH

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Rifampicin-induced toxicity Rats Urine Taurine, glucose TB drug co-treatment induced toxicity Rats Urine Isovalerylglycine, ethylmalonic acid, butyrylglycine, 2methylbutyrylglycine, suberic acid, hydroxyl radical Rats Bile Protoporphyrin IX Human TB Urine Uric acid, mesaconic acid, 5-hydroxypyrazinamide, 5patients hydroxypyrazinoic acid, pyrazinoic acid, isonicotinic acid, 2,3-pyridinedicarboxylic acid, 3-hydroxy-3methyl-Glutaric acid, D-mannitol, pyrazineethanethiol.

Citrate, 2-oxoglutarate

Hepatotoxicity

Liao et al. (2008)

-

Hepatotoxicity

Loots et al. (2005)

(2E,4E)-2,7-Dimethyl-2,4-octadienedioic acid, 2,4dihydroxybutyric acid, 2-aminoadipic acid, 2hydroxyethanesulfonate, 3-hexenedioic acid, 3hydroxy-2-methylpyridine-4,5-dicarboxylate, 4pyridoxic acid, cis-aconitic acid, alphahydroxyisovaleric acid, citramalic acid, citric acid, creatinine, glabrone, hippuric acid, isocitrate, Larabitol, L-erythrulose, myrigalone E, Nmethyltryptamine, phenylbutyrylglutamine, phydroxyphenylacetic acid, pyroglutamic acid, pyrrolidino-[1,2E]-4H-2,4-dimethyl-1,3,5-dithiazine, sebacic acid, suberic acid, succinylacetone, threonic acid, urea, uridine, vanillylmandelic acid, xanthine, hypoxanthine β-hydroxy butyric acid

Hepatotoxicity Hepatotoxicity

Li et al. (2013) Cao et al. (2018)

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LD (120 mg/kg), Low dose; HD, High dose (240 mg/kg); Cr/PCr, creatine-phosphocreatine; GABA, gamma-aminobutyric acid; INH, isoniazid; INH-GLC, β-glucosyl isonicotinylhydrazide; INH-PA, pyruvate isonicotinylhydrazone; NAM, nicotinamide; TMAO, trimethylamine N-oxide; UDP-GalNAc, uridine diphosphate N-acetylglucosamine

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*Decreased (0–48 h) increased (48–96 h); ** Decreased (0–48 h) increased (72–144); *** Decreased (0–48 h) Increased (48–96 h)

3. Metabolomics of rifampicin-induced toxicity

Rifampicin (RIF), a sterilising drug which eliminates active and semi-dormant M. tuberculosis, was discovered in 1966, hence making it the most recent drug addition to the combined TB treatment programme (Ahmad and Mokaddas, 2009). The reported incidence of RIF-related side-effects is rather low. Gastrointestinal reactions such as nausea, vomiting, abdominal cramps and diarrhoea, all of which can be alleviated when the drug is administered with a meal, have been reported in only about 12 % of TB treated patients, and only during the first month of treatment (Tripathi et al., 1991). Additionally, mild dermatological reactions including skin flush, itching and/or a rash, have been observed in about 5 % of all TB patients ingesting

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RIF (Tripathi et al., 1991), while the more severe adverse effects, including thrombocytopenia, occurs at an incidence of about only 0.08 % (Sahu et al., 2015). In the case of RIF, the latter is thought to be caused by a drug-antibody complex, which binds to the platelets’ membrane, thereby leading to platelet cell death via the activation of the complement system (Sahu et al., 2015). Although the intermittent use of RIF (less than 3 times per week) has additionally been

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associated with acute renal failure, shortness of breath, acute hemolytic anaemia, or a ‘flu-like syndrome’ in some patients, these effects are unlikely to occur when the drug is administered

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daily (Tripathi et al., 1991). RIF has also rarely been associated with a dose dependent interference of bilirubin uptake, leading to subclinical, unconjugated hyperbilirubinemia or jaundice, without liver damage (Saukkonen et al., 2006). A more likely occurrence however,

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is that this drug has been shown to promote conjugated hyperbilirubinemia by inhibiting the bile salt exported pump, and thereby interfering with bilirubin excretion (Ramappa and Aithal,

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3.1 Hepatotoxicity

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2013).

The RIF metabolites, 25-deacetyl RIF and 3-formyl RIF, have no known toxic effects on the

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hepatocytes (Nakajima et al., 2011), and RIF itself, only induce very mild hepatotoxicity (with an incidence rate of 1 %) (Sahu et al., 2015). RIF related hepatocellular injury is thought result from a hypersensitivity reaction, and is more commonly associated with higher, intermitted RIF doses (Saukkonen et al., 2006). Accordingly, using a metabolomics research approach, Liao et al. (2008) characterized and compared the effects of a single oral dose of RIF (at a dose of either 50 and 100 mg/kg daily, over a period of 3, 7 and 14 days) on the urine metabolite profiles of rats. Traditional histopathological methods revealed mild hepatotoxicity in the high dose group, and a time-dependent trend was observed when visually analysing the 1H NMR

metabolite profiles via PCA. In comparison to the control group, RIF ingestion additionally resulted in a reduction in urinary citrate and 2-oxoglutarate, accompanied by elevated concentrations of taurine and glucose (Table 1). These observed drug-induced metabolome variations where subsequently associated with an inhibition of the TCA cycle, disruption of both glucose and lipid metabolism, and subsequent inhibition of energy production in the host.

4. Metabolomics of toxicity related to TB drug co-treatment It is important to note, that TB drugs are rarely administered independently, and a combination of toxicity phenotypes is typically observed in TB patients receiving DOTS therapy.

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When investigating the hepatotoxic effects of the INH metabolite; hydrazine, Sanins et al. (1992) similarly identified reduced concentrations of urinary 2-oxoglutarate, and Garrod et al. (2005), reduced concentrations of liver and renal taurine. These similarities could suggest related mechanisms of hepatotoxicity due to hydrazine and RIF-ingestion and is a possible

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future topic of investigation. An important observation pertaining to this is that, although RIFinduced hepatotoxicity is rare, its administration in combination with INH significantly elevates INH-induced hepatotoxicity (Sahu et al., 2015). This is thought to occur due to the fact that

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RIF ingestion leads to the activation of various CYP’s via PXR, and the activation of CYP3A4 in particular, results in enhanced INH metabolism, and the subsequent synthesis of hydrazine

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(Ramappa and Aithal, 2013). To investigate this, Li et al. (2013) compared the UPLC-QTOFMS generated bile metabolomes of INH and RIF co-treated PXR-humanized (hPXR) mice to that of an untreated control group. Significantly elevated levels of PPIX, an intermediate in

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porphyrin biosynthesis and an endogenous hepatotoxin, was detected in the treated sample group. The results of this metabolomics study subsequently confirmed the hypothesized synergistic mechanism by which co-treatment of RIF and INH induce hepatotoxicity, via PXR,

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and a consequent disruption of heme biosynthesis. The detected markers could additionally be used in future to predict, monitor, or even prevent RIF and INH induced liver damage.

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In a similar study, Loots et al. (2005) investigated the effect of the combined TB drug, Rifater (RIF, INH, and PZA), on the urinary metabolome of rats, using a GC-MS approach (Table 1). During-treatment metabolome profiles showed increased free radicals, indicating elevated oxidative stress, and also an increase in organic acids (isovalerylglycine, ethylmalonic acid, butyrylglycine, 2-methylbutyrylglycine, suberic acid) similar to that seen in patients with multiple acyl-CoA dehydrogenase deficiency (MADD).

These results suggest that this

combination of TB drugs is potentially involved in the inhibition of the electron transport chain, as is the case with MADD, and this profile could be used to explain certain TB side effects. The detected metabolite profile could, furthermore, be alleviated by the co-administration of

an anti-oxidant, melatonin, an observation which could assist in the development of less toxic TB drug regimes in future. Similarly, Cao et al. (2018) investigated the combined toxic mechanisms of the four first-line TB drugs included in the DOTS regimen, using a UPLC-MS metabolomics approach, to profile the urine metabolomes of newly diagnosed TB patients, and TB patients receiving the co-treatment for at least one month. Results indicated that various metabolic pathways, including the TCA cycle, amino acids (arginine and proline), purines and redox metabolisms, were affected by these drugs (Table 1). These pathway variations were also shown to differ according to the degree of liver injury in the treated patients, indicating that superoxide generation intensified the hepatotoxic effects of this treatment regime.

Additionally, the group also applied orthogonal partial least square

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discriminant analysis and were able to successfully predict hepatotoxic side effects in TB patients receiving DOTS therapy. This could, therefore, have a direct impact on the treatment of TB patients, since practitioners will be given the opportunity to adjust the treatment regimens before liver damage even occur. The latter study thus proves that, in addition to the elucidation of drug toxicity mechanisms, metabolomics investigations can also have direct

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clinical applications in the field of TB drug toxicology.

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5. Concluding remarks

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Although effective TB drug therapy regimes have already been developed as far back as the 1940s, treatment failure is still a major problem today, with about 15 % of all TB cases being unsuccessfully treated in 2016 (WHO, 2019). Drug toxicity is a major contributor of TB treatment failure, and newer research approaches, such as metabolomics, have recently been

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applied to successfully shed light on previously unknown mechanisms of INH and RIF toxicity as well as combined therapy toxicity. Similar studies, investigating toxicity mechanisms of other TB drugs such as PZA and EMB toxicity are, however, still limited.

Thus far,

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metabolomics has only been used to investigate the toxicity effects of PZA using rat models (Rawat et al., 2018; Zhao et al., 2017) and no metabolomics studies to date have focussed

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EMB toxicity. As seen from the above literature and the summative Table 1, the most TB metabolomics studies done to date have focussed on drug-induced hepatotoxicity, while similar investigations of other toxic effects remain limited. In addition to the elucidation of toxicity mechanism, future studies focussing on the alleviation of these drug-induced toxic effects, could be beneficial for both researchers and clinicians. Such investigation could, for example, include the co-administration of anti-oxidants during treatment, as suggested in a metabolomics study described in section 4 (Loots et al., 2005). From Table 1, it is also evident that only one metabolomics study focussing on TB drug toxicity, has thus far included human

subjects in the sample cohort. Although rodents serve as good models for metabolomic toxicity studies, it could be beneficial to confirm or repeat these studies using non-invasive human sample matrixes such as urine or breath as a means to exclude inter-species metabolome variations. Considering the above, it is apparent that metabolomics has the power to elucidate previously unknown mechanisms of TB drug toxicity, and that similar future investigations can potentially pave the way for the development of more effective, less toxic TB therapy regimes. Funding The financial assistance of the National Research Foundation (NRF) of South Africa for this

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research is gratefully acknowledged (UID: 117459). The opinions expressed and conclusions derived are those of the authors and are not necessarily those of the NRF. Declaration of interests

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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