Chỉ điểm phân tử kháng thuốc sốt rét (Genetic surveillance)

 A. MỘT SỐ CHỈ ĐIỂM PHÂN TỬ KHÁNG THUỐC SỐT RÉT

- MDR (Pf_mdr): nằm trên NST số 5 của KSTSR, multi-drug resistance;

- Kháng artemisinin: Pf_K13, Pv_K12;

- CRT (Pf_crt, Pv_crt): trên NST số 7 của KSTSR, chloroquine-resistance transporter;

- Plasmepsine 4: kháng piperaquine;

- DHFR (dihydro-folate reductase): trên NST số 4 của KSTSR, kháng sulfadoxine;

- DHPS (dihydro-pteroate synthetase): trên NST số 8 của KSTSR, kháng pyrimethamine, proguanil.

B. PHÂN TÍCH VAI TRÒ CỦA PHÂN TÍCH CHỈ ĐIỂM PHÂN TỬ KHÁNG THUỐC (bình luận trên phân tích của Dr Olivo Miotto)

Các nghiên cứu hiệu lực lâm sàng thuốc sốt rét (clinical TES_therapeutic efficacy study) được xem là tốt nhất để biết được một loại thuốc sốt rét, có cần thay đổi thuốc điều trị hay không. Tuy nhiên TES có một số hạn chế:

- Số điểm nghiên cứu hạn chế vì quy trình nghiên cứu kéo dài, phải đảm bảo tuân thủ nghiêm ngặt;

- Kết quả phân tích tương đối chậm;

- Không phân biệt rõ ràng tính kháng đối với từng thành phần trong viên thuốc kết hợp.

Việc phân tích các chỉ điểm phân tử trong DNA ký sinh trùng từ máu bệnh nhân không trực tiếp phản ánh hiệu quả điều trị trên lâm sàng, tuy nhiên giúp khỏa lấp những hạn chế của TES:

- Quy trình lấy mẫu đơn giản, có thể thực hiện ở quy mô rất lớn;

- Kết quả được phân tích nhanh;

- Các chỉ điểm đặc hiệu cho từng loại thuốc, do đó có thể phân biệt được mức độ kháng ở từng thành phần trong viên thuốc kết hợp;

- Là chỉ điểm sớm đối với kháng thuốc.

Tất nhiên, phân tích chỉ điểm phân tử cũng có một số giới hạn:

- Một số thuốc chưa biết chỉ điểm;

- Phân tích phòng thí nghiệm và xử lý số liệu phức tạp.

BS. Châu Khánh

Reference

Dr. Olivo Miotto

Associate Professor, Nuffield Department of Medicine, Oxford University, Oxford Mahidol-Oxford Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.

Here is Dr. Olivo's original letter:

"Dear Dr Khanh,

 

Many thanks for your interest and for your message.

 

My view is that clinical efficacy studies and genetic surveillance are complementary approaches, with some significant differences.

 

As you know, clinical efficacy studies are well established, and *by definition* they are the best at telling us about clinical efficacy. Clinical efficacy is a key parameter that NCMPs need to monitor: if efficacy is low, frontline therapies need changing. However, while they provide crucial knowledge, clinical efficacy studies have some inherent difficulties.

• They must be well controlled and therefore require planning,  good clinical facilities, trained staff, microscopy expertise and patient monitoring- all of these things make them resource-intensive and costly, and impractical to conduct in many low-resource endemic locations. Therefore, only relatively few clinical efficacy studies can be conducted, at a limited choice of locations.
• Since they require advance planning and resource management, clinical studies are not well suited for fast response, e.g. to outbreaks.
• Unfortunately, loss of efficacy is an advanced stage of drug resistance: by the time efficacy studies trigger alarm bells, resistance has reached significant levels, and the battle is likely to be lost. The timing issue is an important one, because the prevalence of resistance in parasites can rise very rapidly, while changes in frontline therapy take time and resources.
• As Prof. Arjen mentioned in his talk, in the case of combination therapies, the efficacy of one drug can prevent you from seeing a loss of efficacy of its partner drug, further delaying the detection of clinical resistance.

 

Genetic surveillance requires the analysis of genetic markers in parasite DNA extracted from patient’s blood. In their operationally simplest form they do not measure clinical outcome (e.g. current projects conducted by GenRe-Mekong with NMCPs in Laos, Vietnam and Cambodia). Therefore, the interpretation of the results relies on known associations between the markers and clinical efficacy.

There are several large studies in this region where both clinical data and data from genetic markers were analyzed, and there is good agreement between them: we find failures primarily where genetic markers of resistance  are also found. Prof Arjen has shown some graphs that show this strong relationship (see Ashley et al. NEJM 2014, and van der Pluijm et al. Lancet Infectious Diseases, 2019). This is not surprising, since this is often how we find markers in the first place (for example, see the discovery of piperaquine resistance markers in Amato et al., Lancet Infectious Diseases, 2016).

 

Although it does not tell us directly about clinical efficacy, genetic surveillance studies overcome some limitations of clinical efficacy studies.

• They do not require as much “bedside” effort. Thanks to advanced technologies, we are able to work with a few drops of blood from a fingerprick, dried on filter paper. This can be done when the patient presents for treatment, requires minimal training and can be implemented in the simplest settings, which allows samples to be collected in the most endemic regions. This means that surveillance can be scaled up to hundreds of sites and thousands of cases.
• Once it is in place, a genetic surveillance project can deliver data with a relatively fast turnaround, and therefore can respond to outbreaks, and to changing epidemiology.
• Because markers are specific to the drugs, we can separate the levels of resistance to different drugs, and monitor levels of resistance even when this has not yet resulted in treatment failures. Therefore, genetic surveillance has the potential for delivering early warning signals to public health.

 

Of course, genetic surveillance has some limitations of its own:

• There are some drugs for which we do not have a reliable marker of resistance, so research must continue to identify potential markers
• Advanced laboratory and data processing techniques are required (GenRe-Mekong is working hard at making these available to endemic countries)

 

There is one important aspect of genetic surveillance that needs to be considered. We do not only monitor drug resistance markers: we also look at many genetic variants that characterize parasite strains. This allows us to identify unusual epidemiological patterns, such as strains that suddenly rise in frequency- even when we do not know that they are resistant to a specific drug, or they are resistant to a drug for which we have not marker.  In other words, we are currently putting effort into making genetic surveillance more “predictive”, to use different approaches to generate knowledge that will avoid NMCPs being taken by surprise. We are still in early stages of development of these new methods, but initial results are very encouraging.

 

I hope you will find this useful.

Best wishes

Olivo"