经过验证的LC-MS/MS面板，用于在小头发样本中量化11种耐药结核病药物
Summary
目前分析患者是否遵守复杂耐药结核病（DR-TB）方案的方法可能不准确且资源密集。我们的方法分析头发，一个易于收集和储存的基质，浓度为11个DR-TB药物。使用LC-MS/MS，我们可以确定亚纳米格药物水平，可用于更好地了解药物依从性。
Abstract
耐药结核病 （DR-TB） 是一个日益严重的公共卫生威胁，评估治疗药物水平可能具有重要的临床益处。血浆药物水平是当前的黄金标准评估，但需要肾切除术和冷链，并捕获只是最近依从。我们的方法使用头发，一个易于收集并反映长期依从性的矩阵，测试11种抗结核药物。我们小组以前的工作表明，头发中的抗逆转录病毒药物水平与艾滋病毒结果有关。我们的DR-TB药物方法使用2毫克的头发（3厘米接近根），这是粉碎和提取甲醇。用一种LC-MS/MS方法分析样品，在16分钟内量化11种药物。11种药物的定量（LLOQs）下限范围从0.01纳克/毫克到1纳克/毫克不等。 药物存在通过比较两种质谱过渡的比率得到确认。样品使用药物与脱硝、15N-或13C标记药物等值的面积比进行量化。我们使用的校准曲线范围为 0.001-100 ng/mg。 该方法在直接观察治疗（DOT）上从DR-TB患者采集的头发样本的方便样本中的应用表明，在11种药物中的9种（异酰胺、苯丙胺、ethambutol、线胶、levofloacin、莫西氟辛、氟胺、贝达奎林、头头体）的线性动态范围内，头发中的药物水平。没有病人使用蛋白苯酰胺，并且测得的液酰胺水平接近其LLOQ（通过进一步的工作，而不是检查依西酰胺的代谢物是否适合监测暴露）。总之，我们描述了头发DR-TB药物的多机解毒板的开发，作为耐药结核病治疗期间治疗药物监测的技术。
Introduction
在21世纪，耐药结核病（DR-TB）是本已薄弱的国家结核病控制计划中不断演变的灾难，仅在过去5年中，确诊病例就翻了一番，占全球抗微生物药物耐药性死亡总数的近三分之一,。与治疗对药物敏感的结核病相比，成功治疗DR-TB通常要求更长、毒性更大的二线治疗方案。此外，DR-TB患者在依从性方面往往存在重大挑战，导致最初出现耐药性。
与HIV感染不同，病毒载量可用于监测治疗，结核病治疗反应的代理终点在个别4级延迟且不可靠。监测患者依从性是亚治疗抗结核药物浓度和治疗失败的重要预测器，也是一项挑战。自我报告的依从性有召回偏见和取悦供应商的愿望55，6。6丸数和药物事件监测系统（MEMS）可以更客观7，但不测量实际药物消耗88，9，10。9,10生物基质中的药物水平可以提供依从性和药代动力学数据。因此，血浆药物水平通常用于治疗药物监测11，12。11,然而，在药物依从性监测方面，血浆水平代表短期接触，在确定适当的依从性参考范围时，受患者体内和患者间变异性的限制。”白衣”效应，在临床或研究访问之前，依从性改善，进一步复杂化血浆水平的能力，提供准确的药物依从模式13。
头发是一种替代生物基质，可以测量长期药物暴露1414，15。15许多药物和内源性代谢物结合到头发蛋白基质从系统循环，因为头发生长。随着头发生长过程中这种动态过程的继续，沉积在头发基质中的药物量取决于药物在循环中的连续存在，使头发成为药物摄入量的极佳时间读出。头发作为生物基质具有另一个优点，即易于收集，无需与血液相比，冷链进行储存和装运。此外，头发是非生物危害的，这提供了额外的可行性优势，在外地。
头发药物水平早已用于法医申请16。在过去十年中，头发抗逆转录病毒（ARV）水平在评估药物在艾滋病毒治疗和预防方面是否得到效用，我们小组对此作出了贡献。头发中的抗逆转录病毒药物水平已被证明是HIV感染治疗结果的最独立预测变量17、18、19、20、21。17,18,19,20,21为了确定DR-TB患者的头发水平在预测治疗结果方面是否具有相同的效用，我们使用LC-MS/MS来开发和验证一种分析小头发样本中11种DR-TB药物的方法。作为对测定性能的初步评估，我们在南非西开普22号对接受直接观察治疗的DR-TB患者的方便样本中测量了DR-TB药物水平。
Protocol
Representative Results
Discussion
我们在这里报告我们开发和验证的方法的协议，用于量化11种抗结核病药物，用于治疗使用LC-MS/MS的小头发样本中的DR-TB。以前没有其他方法来量化头发中的这11种药物，但已经开发、验证和发布。我们的方法可以量化亚纳米克的药物水平，只有20-30发丝长约3厘米（约2毫克），并已经过验证22。头发分析的低重量意味着参与研究的患者可以谨慎参与，并有可能返回重复测试，而不?…
Divulgations
The authors have nothing to disclose.
Acknowledgements
作者要感谢开普敦大学肺研究所的基尔坦·迪达教授、阿里·伊斯梅尔博士和玛丽特杰·普雷托利斯教授为这项研究收集头发样本提供便利。作者还感谢本研究参与者的贡献。
Materials
| 2 mL injection vials | Agilent Technologies | 5182-0716 | |
| 250 uL injection vial inserts | Agilent Technologies | 5181-8872 | |
| Bead ruptor 24 | OMNI International | 19001 | |
| Bead ruptor tubes (2 mL bead kit, 2.8mm ceramic, 2 mL microtubes) | OMNI International | 19628 | |
| Bedaquiline | Toronto Research Chemicals | B119550 | |
| Bedaquiline-d6 | Toronto Research Chemicals | B119552 | |
| Clofazimine | Toronto Research Chemicals | C324300 | |
| Clofazimine-d7 | Toronto Research Chemicals | C324302 | |
| Disposable lime glass culture tubes | VWR | 60825-425 | |
| Ethambutol | Toronto Research Chemicals | E889800 | |
| Ethambutol-d4 | Toronto Research Chemicals | E889802 | |
| Ethionamide | Toronto Research Chemicals | E890420 | |
| Ethionamide-d5 | ClearSynth | CS-O-06597 | |
| Formic acid | Sigma-Aldrich | F0507-100mL | |
| Glass bottles | Corning | 1395-1L | |
| Hot Shaker | Bellco Glass Inc | 7746-32110 | |
| HPLC | Agilent Technologies | Infinity 1260 | |
| HPLC grade acetonitrile | Honeywell | 015-4 | |
| HPLC grade methanol | Honeywell | 230-1L | |
| HPLC grade water | Aqua Solutions Inc | W1089-4L | |
| Isoniazid | Toronto Research Chemicals | I821450 | |
| Isoniazid-d4 | Toronto Research Chemicals | I821452 | |
| LC column, Synergi 2.5 um Polar RP 100 A 100 x 2 mm | Phenomenex | 00D-4371-B0 | |
| LC guard cartridge | Phenomenex | AJ0-8788 | |
| LC guard cartridge holder | Phenomenex | AJ0-9000 | |
| LC-MS/MS quantitation software | Sciex | Multiquant 2.1 | |
| Levofloxacin | Sigma-Aldrich | 1362103-200MG | |
| Levofloxacin-d8 | Toronto Research Chemicals | L360002 | |
| Linezolid | Toronto Research Chemicals | L466500 | |
| Linezolid-d3 | Toronto Research Chemicals | L466502 | |
| Micro centrifuge tubes | E&K Scientific | 695554 | |
| Moxifloxacin | Toronto Research Chemicals | M745000 | |
| Moxifloxacin-13C, d3 | Toronto Research Chemicals | M745003 | |
| MS/MS | Sciex | Triple Quad 5500 | |
| OPC 14714 | Toronto Research Chemicals | O667600 | |
| Pretomanid (PA-824) | Toronto Research Chemicals | P122500 | |
| Prothionamide | Toronto Research Chemicals | P839100 | |
| Prothionamide-d5 | Toronto Research Chemicals | P839102 | |
| Pyrazinamide | Toronto Research Chemicals | P840600 | |
| Pyrazinamide-15N, d3 | Toronto Research Chemicals | P840602 | |
| Septum caps for injection vials | Agilent Technologies | 5185-5862 | |
| Turbovap LV evaporator | Biotage | 103198/11 |
References
- Kurbatova, E. V., et al. Predictors of poor outcomes among patients treated for multidrug-resistant tuberculosis at DOTS-plus projects. Tuberculosis (Edinb). 92, 397-403 (2012).
- Dheda, K., et al. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respiratory Medicine. , (2017).
- Berg, K. M., Arnsten, J. H. Practical and conceptual challenges in measuring antiretroviral adherence. Journal of Acquired Immunodeficiency Syndromes (JAIDS). 43, 79-87 (2006).
- Kagee, A., Nel, A. Assessing the association between self-report items for HIV pill adherence and biological measures. AIDS Care. 24 (11), 1448-1452 (2012).
- Haberer, J. E., et al. Adherence to antiretroviral prophylaxis for HIV prevention: a substudy cohort within a clinical trial of serodiscordant couples in East Africa. PLoS Medicine. 10 (9), 1001511 (2013).
- Pullar, T., Kumar, S., Tindall, H., Feely, M. Time to stop counting the tablets. Clinical Pharmacology & Therapeutics. 46 (2), 163-168 (1989).
- Liu, H., et al. A comparison study of multiple measures of adherence to HIV protease inhibitors. Annals of Internal Medicine. 134 (10), 968-977 (2001).
- Wendel, C., et al. Barriers to use of electronic adherence monitoring in an HIV clinic. Annals of Pharmacotherapy. 35, 1010-1101 (2001).
- Ruiz, J., et al. Impact of voriconazole plasma concentrations on treatment response in critically ill patients. Clinical Pharmacology & Therapeutic. , (2019).
- Saktiawati, A. M., et al. Optimal sampling strategies for therapeutic drug monitoring of first-line tuberculosis drugs in patients with tuberculosis. Clinical Phamacokinetics. , (2019).
- Podsadecki, T. J., Vrijens, B. C., Tousset, E. P., Rode, R. A., Hanna, G. J. “White coat compliance” limits the reliability of therapeutic drug monitoring in HIV-1-infected patients. HIV Clinical Trials. 9 (4), 238-246 (2008).
- Cuypers, E., Flanagan, R. J. The interpretation of hair analysis for drugs and drug metabolites. Clinical Toxicology. 56 (2), 90-100 (2018).
- Knitz, P., Villain, M., Crimele, V. Hair analysis for drug detection. Therapeutic Drug Monitoring. 28 (3), 442-446 (2006).
- Barroso, M., Gallardo, E., Vleira, D. N., Lopez-Rivadulla, M., Queiroz, J. A. Hair: a complementary source of bioanalytical information in forensic toxicology. Bioanalysis. 3 (1), 67-79 (2011).
- Gandhi, M., et al. Atazanavir concentration in hair is the strongest predictor of outcomes on antiretroviral therapy. Clinical Infectious Diseases. 52 (10), 1267-1275 (2011).
- Koss, C. A., et al. Hair concentrations of antiretrovirals predict viral suppression in HIV-infected pregnant and breastfeeding Ugandan women. AIDS. 29 (7), 825-830 (2015).
- Pintye, J., et al. Brief Report: Lopinavir Hair Concentrations Are the Strongest Predictor of Viremia in HIV-Infected Asian Children and Adolescents on Second-Line Antiretroviral Therapy. Journal of Acquired Immune Deficiency Syndromes (JAIDS). 76 (4), 367-371 (2017).
- Baxi, S. M., et al. Nevirapine Concentration in Hair Samples Is a Strong Predictor of Virologic Suppression in a Prospective Cohort of HIV-Infected Patients. PLoS One. 10 (6), 0129100 (2015).
- Gandhi, M., et al. Antiretroviral concentrations in hair strongly predict virologic response in a large HIV treatment-naive clinical trial. Clinical Infectious Diseases. 5, 1044-1047 (2019).
- Gerona, R., et al. Simultaneous analysis of 11 medications for drug resistant TB in small hair samples to quantify adherence and exposure using a validate LC-MS/MS panel. Journal of Chromatography B. 1125, 121729 (2019).
- Metcalfe, J., et al. Association of anti-tuberculosis drug concentration in hair and treatment outcomes in MDR- and XDR-TB. European Respriatory Journal Open Research. 5 (2), (2019).
- Metcalfe, J. Z., O’Donnell, M. R., Bangsberg, D. R. Moving Beyond Directly Observed Therapy for Tuberculosis. PLoS Medicine. 12 (9), 1001877 (2015).
