Re-defining Multidrug Resistant Tuberculosis Based on Clinical Response to 1
Combination Therapy 2
Tawanda Gumbo1,2, Jotam G Pasipanodya1, Peter Wash, André Burger3, Helen McIlleron4. 3
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1Office of Global Health and 2Department of Medicine, UT Southwestern Medical Center, Dallas, 5
Texas, USA, 3Brewelskloof Hospital, Worcester, South Africa, and the 1Division of 6
Pharmacology, Department of Medicine, University of Cape Town, Observatory, South Africa 7
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Corresponding author: Helen McIlleron, MBChB, PhD 9
Division of Clinical Pharmacology 10
Department of Medicine, University of Cape Town 11
Observatory, Cape Town 7925, South Africa 12
E-mail: [email protected] 13
Phone: +27-21-406-6292 14
Fax: +27-21-448-1989 15
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Key words: multi-drug resistant tuberculosis, isoniazid, rifampin, critical concentrations, 17
classification and regression tree analysis. 18
Running title: A new definition of MDR-TB 19
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AAC Accepts, published online ahead of print on 4 August 2014Antimicrob. Agents Chemother. doi:10.1128/AAC.03549-14Copyright © 2014, American Society for Microbiology. All Rights Reserved.
ABSTRACT 25
In tuberculosis treatment, susceptibility is defined by a critical concentration of 1.0 mg/L for 26
rifampin and 0.2 mg/L or 1.0 mg/L for low- and high-level isoniazid resistance, based on 27
epidemiologic cut-off method of the distribution of isolates’ MICs. However, 28
pharmacokinetics/pharmacodynamics based clinical trial simulations suggested that the 29
breakpoints should be 0.0625 mg/L for rifampin and 0.0312 mg/L or 0.125 mg/L for isoniazid. 30
We examined outcomes in 36 patients with drug-susceptible tuberculosis, who had rifampin and 31
isoniazid MICs performed and also had plasma drug concentrations measured, who were part of 32
a prospective cohort study in Western Cape, South Africa. We performed classification and 33
regression tree analysis to identify clinical and laboratory factors which predicted 2-month 34
sputum conversion rates and long-term clinical outcomes. Poor long-term clinical outcomes 35
were defined as microbiological failure, relapse or death, with 2 years of follow up. Peak drug 36
concentrations and area under the concentration-time curve were most predictive of outcomes, 37
and constituted the primary node, similar to our findings with the larger cohort. However, 38
rifampin MIC and isoniazid MIC improved the predictive capacity of the primary decision node 39
by 20% and 17%, respectively in these 36 patients. The rifampin MIC cut-off above which there 40
was therapy failure was 0.125 mg/L, while that for isoniazid was 0.0312 mg/L, which are similar 41
to those derived in clinical trial simulations. The critical concentrations used to define multi-42
drug resistance for clinical decision-making should take into account clinical outcomes. 43
44
45
46
47
48
Patients with drug resistant tuberculosis, especially multi-drug resistant- tuberculosis (MDR-49
TB; defined as tuberculosis resistant to both isoniazid and rifampin) and extremely drug-50
resistant tuberculosis (XDR-TB; MDR-TB with additional resistance to fluoroquinolones and 51
injectable compounds) need to be accurately categorized from the outset as they need specific 52
management with second-line regimens (1-5). Hence it is crucial that resistance testing 53
identifies infections with a susceptibility threshold beyond which patients receiving the first-line 54
regimen with standard doses of rifampicin and isoniazid will respond poorly. Resistance to 55
isoniazid and rifampin is said to be present when ≥1% of Mycobacterium tuberculosis culture 56
grows in media supplemented with the critical concentration of each drug. The critical 57
concentrations of rifampin and isoniazid were derived from the MICs of ≥95% of wild-type 58
isolates of M. tuberculosis, termed epidemiologic cut-off values (6-8). While extensively used, as 59
evidenced by the regular release of MDR-TB worldwide figures by the World Health 60
Organization (3), it is nevertheless unclear if the epidemiologic cut-off values are predictive of 61
clinical responses, and if they are the most accurate. Here, we investigated this possibility based 62
on data from a prospective cohort study. 63
64
Another approach that has been used to identify susceptibility breakpoints is pharmacokinetic-65
pharmacodynamic (PK/PD) based derivation that takes into account pharmacokinetic 66
variability encountered for each drug (9). PK/PD studies in pre-clinical models such as the 67
hollow fiber model as well as in clinical studies of tuberculosis patients have identified peak-to-68
MIC and area under the concentration-time curve (AUC)-to-MIC ratios below which there is 69
poor outcome (10-14). In addition, pharmacokinetic variability has been shown to be one of the 70
important determinants of therapy failure in patients, and has delineated optimal AUCs and 71
peak concentrations below which patients fail therapy (15,16). Therefore, we have proposed that 72
the critical concentration should be defined as the MIC above which maximum tolerated doses 73
fail to effectively kill M. tuberculosis at site of infection (17). If the recommended doses in the 74
treatment regimen are unable to effectively kill bacilli in patients because of their MIC, then that 75
MIC defines clinically meaningful drug resistance. Monte Carlo simulations, which utilized 76
PK/PD exposures associated with optimal outcomes and antibiotic pharmacokinetic variability 77
encountered in patients, led to the proposal that rifampin critical concentrations should be 78
lowered from the epidemiologic cut-off derived value of 1.0 mg/L to 0.0625 mg/L, and isoniazid 79
low- and high-level resistance concentrations from 0.2/1.0 mg/L to 0.0312/0.125 mg/L in 80
Middlebrook media (17). Here, we used an agnostic machine learning method to identify the 81
rifampin and isoniazid MICs above which patients fail therapy during treatment with short-82
course chemotherapy in the clinic. 83
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METHODS 85
86
One hundred and forty two patients with sputum culture positive tuberculosis were enrolled in a 87
pharmacokinetic and pharmacodynamic study at Brewelskloof Hospital in Worcester, Western 88
Cape, South Africa, as described in prior studies (15,18). Patients were recruited between August 89
1999 and February 2002. Thirty six of these 142 patients, randomly chosen, also had isoniazid 90
and rifampin MICs identified in the isolates at the time of diagnosis. Patients were treated using 91
the following daily doses during the intensive phase of therapy: 300 mg of isoniazid, 20-35 92
mg/kg pyrazinamide, 15 mg/kg ethambutol, and 600mg of rifampicin daily if they weighed 93
>50kg or 450 mg if they weighed less. Patients with prior tuberculosis also received intra-94
muscular streptomycin (1 g if they weighed more than 55 kg, 0.75 g if they weighed 38-54kg, or 95
0.5g if they weighed≤37kg). In the continuation phase all patients received the same isoniazid 96
and rifampin doses but for 5 in 7 days and patients with prior tuberculosis were continued on 97
ethambutol. All patients were hospitalized during the first two months of therapy to ensure 98
directly observed therapy (DOTS), for reasons outlined in the primary pharmacokinetic study 99
publication (18). Pharmacokinetic studies were performed over 24 hours during the eighth week 100
of therapy. The compartmental pharmacokinetic analyses have been published elsewhere (15). 101
Sputum microscopy and culture (using liquid cultures in the BACTEC 460 instrument) were 102
performed after eight weeks of treatment. Patients were then followed prospectively for sputum 103
conversion and long-term outcomes over 2 years 104
105
All patients’ isolates had direct sensitivity testing performed using BACTEC. In 36 patients, 106
isoniazid and rifampin MIC assays were performed on the initial isolate at diagnosis. Isoniazid 107
susceptibility was determined in the BACTEC system using the following concentrations: 108
0.0125, 0.025, 0.05, 0.1 and 0.2 mg/L. For rifampin the following concentrations were 109
examined: 0.06, 0.125, 0.25, 0.5, 1.0 and 2.0 mg/L. MICs of pyrazinamide or ethambutol were 110
not identified. 111
112
Classification and regression tree (CART) analysis is a non-parametric method utilizing 113
recursive partitioning to classify data (19-21), which we and others have used for clinical 114
decision making to examine outcomes in several infectious diseases studies (15,22-25). We were 115
interested in classification of the 36 patients into two categories: therapy failure versus success. 116
Two clinical outcome measures were utilized: sputum microbiology status at two months as well 117
as long term outcomes. Poor long-term outcomes were defined as either relapse or 118
microbiologic failure, or death, for up to 2 tears of follow up since the start of therapy (15). 119
Several predictors of outcome were examined, including AUC, peak concentrations, trough 120
concentrations, isoniazid MIC, rifampin MIC, age, gender, weight, HIV status, and treatment 121
with streptomycin. CART analysis examines all these potential predictors of outcome, and 122
examines all possible threshold values of the predictors, to create a tree. The step-by-step 123
description of this analytic method for tuberculosis therapy were described in detail in a prior 124
publication (15). Briefly, the upside tree created starts with a root node, which is the most 125
important potential predictor or decision node. Daughter nodes are added to the tree, and a 126
score of how much they improve the primary decision node (as a % score of the primary node) is 127
computed. For each outcome, maximal trees were generated by split each daughter node so that 128
each class was homogenous in the examined outcome until further splits were not possible. 129
Maximal trees are important in identifying data structure as well clinically meaningful 130
interactions between covariates, particularly among fewer patients (small-sized daughter 131
nodes). We then pruned the maximal trees based on relative misclassification costs, complexity 132
and maximization of parsimony. Next, we performed a 10-fold validation. In this process, the 133
dataset is randomly split 10 times in order to construct optimal trees, to identify how predictive 134
the primary analysis was with these randomly created “test” data sets. This process obviates the 135
need for a validation data set. 136
137
RESULTS 138
139
The full clinical characteristics of the 36 patients in whom isoniazid and rifampin MICs were 140
performed, are shown in Table 1. The characteristics are similar to the entire data set of 142 141
patients (15), confirming that this subset of patients was randomly chosen from the larger data 142
set. The distribution of isoniazid MICs is shown in Figure 1. The mean isoniazid MIC was 143
0.07±0.04 mg/L. The epidemiologic cut-off value was 0.2 mg/L. The distribution of rifampin 144
MICs is shown in Figure 2. The mean rifampin MIC was 0.28±0.13 mg/L. The rifampin 145
epidemiologic cut-off value was 0.5 mg/L. 146
147
As regards to the two month sputum conversion rates, 18/36 (50%) patients had a positive 148
culture or smear at 8 weeks. The factors most predictive of 2-month sputum conversion 149
included peak pyrazinamide, rifampin and isoniazid concentrations, as identified in the larger 150
142 patient study (15). However, in the 36 patients, isoniazid and rifampin MICs were also 151
predictive of 2-month sputum conversion, with MIC thresholds shown in Table 2. While 2-152
month sputum conversion is an important surrogate, the gold standard of antituberculosis 153
therapy efficacy is still long-term outcomes. As regards to the long term outcomes, pyrazinamide 154
AUC was the primary root node, followed by rifampin AUC, and the isoniazid AUC, consistent 155
with findings from the entire dataset of 142 patients. However, the next root node was of 156
rifampin MIC, followed by isoniazid MIC. Rifampin and isoniazid MICs improved the primary 157
decision node by 20% and 17%, respectively. The rifampin and isoniazid cut-off points 158
associated with outcome are shown in table 2. However, in standard assays, MICs are performed 159
based on two-fold dilution steps, so that the nearest observed MIC that fulfills the non-strict 160
inequality values shown in Table 2 was chosen as the breakpoint MIC (Table 2). The results 161
held in the 10-fold validation process. Since predictive power is defined as performance of the 162
training set–derived tree on the test dataset, our results suggest a good predictive role for 163
therapy failure for these rifampin and isoniazid MICs. As can be seen in the table, the isoniazid 164
MIC breakpoints for patients who failed were 0.0312 mg/L while that for rifampin was 0.0625 165
mg/L. 166
167
DISCUSSION 168
169
Isoniazid and rifampin MICs were predictive of clinical outcome, both at the 2-month time point 170
and for long-term outcomes. These data are consistent with classic antimicrobial 171
pharmacokinetic/pharmacodynamics theory, which is that the MIC of a bacterial species is an 172
important determinant of outcome (26-29). Therefore, similar to many other pathogens such as 173
standard gram negative and gram positive bacteria, MICs of anti-tuberculosis drugs affect 174
clinical outcome. The MIC’s effect on microbial kill is considered relative to the drug 175
concentration achieved at site of infection. The drug concentrations achieved in patients have a 176
ceiling at the maximum tolerated dose, and are driven mainly by between-patient 177
pharmacokinetic variability. As the MIC rises in the face of that ceiling concentration, the ratio 178
of AUC/MIC or peak/MIC fall, leading to less and less kill. Eventually, an MIC is reached above 179
which microbial killing is not effective in most patients and which defines clinical resistance. 180
This MIC above which therapeutic failure occurs is not necessarily linked to the 95% 181
epidemiologic cut-off value derived from the MIC distribution. Indeed, there should be no 182
mathematical or physiological reason to expect patient response to be linked to Gaussian 183
parameters of M. tuberculosis isolates’ drug MICs. On the other hand, a shift in the 95% 184
epidemiologic cut-off value at different time periods indicates that the population of isolates 185
from the locale is moving more and more towards drug resistance, making it an excellent index 186
for epidemiologic and public health work tracking of MDR-TB and XDR-TB. Our current study 187
suggests that for clinical decision making during combination therapy, however, susceptibility 188
breakpoint values should be lowered to 0.0312 mg/L for isoniazid, and 0.125 mg/L for rifampin, 189
and do not coincide with the 95% epidemiologic cut-off value. The implication is that MDR-TB 190
rates in the world are likely multiple-fold higher than currently assumed. 191
192
Our results redefine MDR-TB, and by extension XDR-TB, given that these definitions are 193
dependent on critical concentrations of isoniazid and rifampin. In the case of rifampin, four sets 194
of case reports from New Zealand, Australia and the Netherlands, in which a total of 11 clinical 195
isolates from these 3 centers had mutations in the β subunit of RNA polymerase (rpoB) flagged 196
as rifampin-resistant by Gene-Xpert® technology, were found to have rifampin MICs of 0.125-197
1.0 mg/L and failed rifampin containing multiple drug therapy (30-32). The authors termed this 198
“phenotypically occult” resistance. We propose, instead, that the rifampin breakpoint should in 199
fact be lower than current standards, at ≤0.125 mg/L. Moreover, our proposed breakpoint is 200
based on failure of patients to respond to therapy, and is therefore not defined by chromosomal 201
mutations as is the case with Gene-Xpert. That is because not all drug-resistance is due to 202
mutations; some M. tuberculosis isolates have naturally high MICs, while other mechanisms of 203
drug resistance such as efflux pump induction could also lead to drug-resistance (33-36). There 204
have not been any similar case reports for isoniazid as the 11 cases for rifampin since isoniazid 205
does not as of yet have a rapid molecular test to identify resistant mutants. Nevertheless, 206
mechanisms such as efflux pumps are also known to play a role in isoniazid resistance, which 207
means the final definition of isoniazid resistance will ultimately have to be based on phenotypic 208
tests such as MICs (11,13,37,38). Our current findings suggest the critical concentration for 209
isoniazid should also be lowered for clinical decision making. In essence, there is now clinical 210
support to change these critical concentrations that define MDR-TB. These new concentrations 211
should be considered for clinical decision making by the several bodies important in the clinical 212
care of tuberculosis around the world such as the WHO and STOP TB, as well as by groups 213
designing assays for diagnosis of MDR-TB. 214
215
The approach using hollow fiber PK/PD and pharmacokinetic variability in Monte Carlo 216
simulations to identify provisional and definitive susceptibility breakpoints of antituberculosis 217
agents (17) identified virtually the same breakpoints as those identified in our current clinical 218
study. Similarly, the breakpoint of pyrazinamide was correctly identified using this method, and 219
was also recently identified using CART to be identical (25). This is noteworthy because CART 220
analyses did not pre-specify the susceptibility breakpoints that should be selected but were 221
“agnostic” in both identifying MIC as a predictor of clinical outcome from among several clinical 222
factors, but also calculated threshold MICs that classifies patients into those who fail or succeed 223
therapy without relying on a prior choice. Therefore, the pharmacometric pathway consisting of 224
(a) pre-clinical PK/PD studies of monotherapy to identify optimal drug exposures in the hollow 225
fiber or other pre-clinical models, followed by (b) use of population pharmacokinetics in Monte 226
Carlo experiments, and (c) the choice of a susceptibility breakpoint based on >10% of patients 227
failing to achieve optimal exposures, is validated for setting susceptibility breakpoints of anti-TB 228
drugs. We propose its use for the new and experimental antituberculosis therapies currently 229
being studied. 230
Our study has several limitations. First is the size of the clinical study, which could limit 231
generalizability of the findings. However, CART has been able to correctly identify thresholds 232
with similarly small populations in the past (24,25,39). Second, several other clinical factors also 233
determine clinical outcome, including drug concentrations, cavitary disease, and bacterial 234
burden. However, these factors need not exclude a role for MICs in outcome. Indeed, our CART 235
analysis also examined some of these as possible predictors, and with the exception of drug 236
concentrations, they were outranked by MICs. Third, one potential limitation of CART is over 237
fitting and biasing toward covariates with many possible splits. Thus our findings should be 238
taken with these three factors in context. Nevertheless, cross-validation identified the same MIC 239
threshold values, which were virtually identical to Monte Carlo simulations results published 5 240
years earlier (17). This means that the same breakpoints have now been identified using two 241
completely independent methods. In the case of rifampin, retrospective case reports lead to the 242
same conclusion, adding a third independent method. Thus, critical concentrations of 0.125 243
mg/L for rifampicin, and 0.0312 mg/L for isoniazid should be used to define MDR-TB. Such 244
PK/PD evaluation should form the basis for accurate susceptibility breakpoints. The continued 245
use of breakpoints which disregard the drug exposures in patients administered recommended 246
doses, will lead to incorrect categorization of patients and treatment with inappropriate 247
regimens (40). 248
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FUNDING 255
This work was supported by the National Institutes of Health via the NIH Director New 256
Innovator Award (National Institutes of General Medical Sciences DP2 OD001886 for JGP and 257
TG, and the National Institute of Allergy and Infectious Diseases R01AI079497 for JGP and TG). 258
Funding for recruitment of patients and for Helen McIlleron was provided by the Division of 259
Pharmacology of the University of Cape Town, and the Medical Research Council of South 260
Africa. 261
262
CONFLICTS OF INTEREST 263
TG founded Jacaranda Biomed. JGP and HM have no conflicts of interest. 264
265
ACKNOWLEDGEMENTS. 266
We would like to acknowledge the patients who participated in the study, the nursing staff of 267
Brewelskloof Hospital, Western Cape, for taking care of patients, and Dr Andrew Whitelaw for 268
MIC determination. We would also like to acknowledge the University of Cape Town’s 269
Department of Medicine and UT Southwestern Medical Center’s Office of Global Health for 270
making this collaboration possible. 271
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Mycobacterium tuberculosis through Efflux. Am.J.Respir.Crit Care Med. 184:269-76. 399
37. Gumbo, T., A. Louie, W. Liu, P. G. Ambrose, S. M. Bhavnani, D. Brown, and 400
G. L. Drusano. 2007. Isoniazid's bactericidal activity ceases because of the emergence 401
of resistance, not depletion of Mycobacterium tuberculosis in the log phase of growth. 402
J.Infect.Dis. 195:194-201. 403
38. Machado, D., I. Couto, J. Perdigao, L. Rodrigues, I. Portugal, P. Baptista, B. 404
Veigas, L. Amaral, and M. Viveiros. 2012. Contribution of Efflux to the Emergence 405
of Isoniazid and Multidrug Resistance in Mycobacterium tuberculosis. PLoS.One. 406
7:e34538. 407
39. Gumbo, T., J. Hiemenz, L. Ma, J. J. Keirns, D. N. Buell, and G. L. Drusano. 408
2008. Population pharmacokinetics of micafungin in adult patients. 409
Diagn.Microbiol.Infect.Dis. 60:329-331. 410
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evidence to change susceptibility breakpoints for first-line anti-tuberculosis drugs. Int J 412
Tuberc Lung Dis 16:706-707. 413
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419
Figure 1. Isoniazid MIC distribution in isolates from 36 patients. 420
Isoniazid MIC (mg/L)
Per
cent
of c
linic
al is
olat
es
0.0125 0.025 0.05 0.1 0.20
20
40
60
421
The Gaussian distribution has a right skew, and a slightly higher epidemiologic cut-off value 422
than in the literature (6). 423
424
425
426
427
428
429
430
431
432
433
434
435
436
Figure 2. Rifampin MIC distribution in isolates from 36 patients. 437
438
Rifampin MIC (mg/L)
Per
cent
of c
linic
al is
olat
es
0.03125 0.0625 0.125 0.25 0.5 1 20
10
20
30
40
50
60
439
Gaussian distribution of rifampin MICs in patients, means that there is no one “MIC” to a drug 440
in clinical isolates. Thus the standard notion of stating that rifampin clinical isolates have an 441
MIC of say 0.125 mg/L is meaningless. 442
443
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447
448
449
450
451
452
Table 1. Clinical and demographic factors in 35 patients treated for tuberculosis in 453
Cape Town, South Africa. 454
Characteristic Estimate or
median
Range or
percentage
Sex-female (%) 23 65.71%
Age in years 35 20-71
Self-identified “race” or ethnic group
Mixed Race South African 31 88.57%
Black South African 4 11.43%
Weight (kg)
Weight 47.3 30-68
HIV infection (%) 3 8.57%
Dose and range in mg/kg
Rifampin 10.71 8.38-15.00
Isoniazid 6.39 4.41-10.00
Pyrazinamide 35.14 19.69-50.00
Ethambutol 23.87 12.88-30.77
Prior tuberculosis (%) 23 65.71%
Hemoglobin g/dL (range) 12.00 7.40-14.00
White cell count x109 cells/mL 8.60 3.80-25.70
Platelet count x109 cells/mL 410.0 66.0- 813.0
ESR in mm/hr 58 12-136
Total protein in g/L 77 62-92
Albumin in g/L 34.00 21-43
455
Table 2. Classification and regression tree analysis derived MIC breakpoints in 36 456 patients. 457
Two month sputum conversion Cut-off point 2 log scale
Long term outcomes Cut-off point 2 log scale
Isoniazid MIC (mg/L) ≤0.150 0.125 ≤0.038 0.0312 Rifampin MIC (mg/L) ≤0.188 0.125 ≤0.38 0.25 458
459
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461
462
463