Description
Treatment for All and Treatment as Prevention are crucial for the targets of eradicating HIV by 2030. However, emergence of antiretroviral
(ARV) drug resistance (DR) threatens these objectives by compromising viral suppression and contributing to continued HIV transmission.
Several reports indicate an alarming increase of DR strains in Africa. One of the major obstacles to prevent this increase is that ARV DR testing
in Africa is not used in patient’s follow-up and thus knowledge about circulating drug resistance mutations (DRM) is limited. Furthermore, there
is growing evidence that many factors contribute to suboptimal ARV therapy in these countries, such as suboptimal adherence and problems of
drug stock out. While such resistant variants initially circulate undetected in African countries, later they can be exported to other countries and
initiate a global problem of ARV drug resistance.
Herein, we will use a transdisciplinary approach to generate new evidence about ARV DR variants. We will combine data collection through
portable high-throughput sequencing, statistical analyses, data mining and machine learning to generate accurate and up-to-date information
about circulating DR variants, treatment experience and levels of transmitted (TDR) and acquired drug resistance (ADR).
First, we will collect socio-behavioral, clinical and genomic data from 3 African low- and middle-income countries (LMIC) – São Tomé e Príncipe,
Cape Verde and Mozambique - where Treatment for All has been implemented but genomic data from drug naïve and treated patients is scarce
or inexistent.
Second, we will analyse this data to generate evidence about HIV-1 subtypes and DRMs circulating in these countries, as well as about
determinants of emergence of TDR and ADR.
Third, we will combine this with data stored in other databases to generate machine learning models that identify treatment experience based
on the HIV-1 patients genomic signatures. These models will then be applied to different datasets to generate more accurate estimates about
transmitted and acquired antiretroviral drug resistance. Finally, a web platform will be provided for open access use of this tool and this
knowledge will be used to refine HIV-1 first-line treatment guidelines in these countries.
Overarching objectives of this project will include to prevent dissemination of ARV DR HIV-1 variants, to transfer theoretical and practical
knowledge to these LMIC concerning surveillance of ARV DR and to develop evidence-based HIV-1 treatment guidelines and public health
policies.
(ARV) drug resistance (DR) threatens these objectives by compromising viral suppression and contributing to continued HIV transmission.
Several reports indicate an alarming increase of DR strains in Africa. One of the major obstacles to prevent this increase is that ARV DR testing
in Africa is not used in patient’s follow-up and thus knowledge about circulating drug resistance mutations (DRM) is limited. Furthermore, there
is growing evidence that many factors contribute to suboptimal ARV therapy in these countries, such as suboptimal adherence and problems of
drug stock out. While such resistant variants initially circulate undetected in African countries, later they can be exported to other countries and
initiate a global problem of ARV drug resistance.
Herein, we will use a transdisciplinary approach to generate new evidence about ARV DR variants. We will combine data collection through
portable high-throughput sequencing, statistical analyses, data mining and machine learning to generate accurate and up-to-date information
about circulating DR variants, treatment experience and levels of transmitted (TDR) and acquired drug resistance (ADR).
First, we will collect socio-behavioral, clinical and genomic data from 3 African low- and middle-income countries (LMIC) – São Tomé e Príncipe,
Cape Verde and Mozambique - where Treatment for All has been implemented but genomic data from drug naïve and treated patients is scarce
or inexistent.
Second, we will analyse this data to generate evidence about HIV-1 subtypes and DRMs circulating in these countries, as well as about
determinants of emergence of TDR and ADR.
Third, we will combine this with data stored in other databases to generate machine learning models that identify treatment experience based
on the HIV-1 patients genomic signatures. These models will then be applied to different datasets to generate more accurate estimates about
transmitted and acquired antiretroviral drug resistance. Finally, a web platform will be provided for open access use of this tool and this
knowledge will be used to refine HIV-1 first-line treatment guidelines in these countries.
Overarching objectives of this project will include to prevent dissemination of ARV DR HIV-1 variants, to transfer theoretical and practical
knowledge to these LMIC concerning surveillance of ARV DR and to develop evidence-based HIV-1 treatment guidelines and public health
policies.
| Awarded date | 1 Jan 2022 |
|---|---|
| Degree of recognition | National |
| Granting Organisations | Fundação para a Ciência e a Tecnologia |