By the end of 2018, the Joint United Nations Programme on AIDS [1] estimated that about 37.9 million people were infected with HIV worldwide. Although recent UNAIDS data indicate that about 24.7 million people are on antiretroviral therapy (ART), more than double the number reported as recently as 2012 [1], this is still not on track to meet the 30 million by 2020. Of those infected, 7.7 million were in South Africa (SA) [1]. The South African governement introduced treatment for HIV infection in the public health sector in 2004. At that time, only patients with CD4+ T-cell counts of 200 cells μL−1 or less were eligible for treatment. Since then, the eligibility criteria to enter the treatment programme has evolved. In light of tangible evidence that treatment significantly reduces HIV transmission at the population level [2, 3]; SA introduced Universal Test and Treat (UTT) in September 2016, a move whereby all tested and prepared persons enter into treatment irrespective of CD4+ T-cell count. The goal of UTT led to the UNAIDS 90-90-90 mantra adopted in 2016, which in practical terms translates to: 90% of the population should know their HIV status, 90% of those with known HIV infection status should be on treatment, and 90% of those on treatment should have sustained suppressed viral loads. The expected outcomes include a significant decrease in mobidity and mortality, and a significant reduction in viral transmission. This is evidenced by the number of AIDS related deaths which has declined by 43% since 2003, the likelihood of viral transmission now reduced to only 4%, and subsequently an end to the epidemic by 2030 [1].

Currently, SA has the largest HIV treatment programme in the world, with about 5 milion people on treatment as of December 2018. In an effort to make ARV available to those in need, major treatment centers are located in large hospitals serving urban dwellers while rural areas have access to primary health care centers within their communities. Treatment initiation occurs mostly at the primary health care level after clinical and psychosocial assessments. At ART initiation, a CD4+ T-cell count is done, then followed-up at six months, and thereafter annually. Meanwhile, a viral load is performed at six months post treatment initiation and then annually [4]. For SA, UNAIDS 2019 data show that 90% of people are aware of their HIV status of which 68% are on treatment and of which 87% are virally suppressed. In essence, 62% of all those living with HIV are on treatment, and 54% of all living with HIV are virally suppressed. Following evidence that dolutegravir, an integrase strand transfer inhibitor, has a higher genetic resistance barrier than non-nucleoside reverse transcriptase inhibitors, in November 2019 the recommended standard initial treatment regimen was switched to the fixed dose combination comprising Tenofovir–Lamivudine–Dolutegravir (TLD), a change from Tenofovir–Lamivudine–Efavirenz [5, 6].

There are several key assumptions in the operationalization of UTT. These assumptions include: firstly, that all persons diagnosed and accessing treatment have viruses which are susceptible to the recommended first line treatment regimen. In this assumption, the scenario is that the prevalence of circulating drug resistant viruses in the pre-treated population is negligible to cause a significant negative impact on treatment outcomes. Secondly, that the proportion of patients with prior exposure to treatment from one locality and re-initiating treatment in another locality, without disclosing their prior exposure, is negligible to impact on treatment outcomes. Thirdly, there is a high level of adherence to treatment coupled to minimal acquired resistance to achieve sustained viral load suppression. Fourthly, the pharmacokinetics and pharmacodynamics of antiretrovirals is optimal in the population; and lastly, that in a country, such as South Africa, where there is appreciable concomitant use of ARV and herbal preparations, the efficacy of ARV is not compromised.

As a result of these assumptions, patients are not assessed for the following prior to treatment initiation: exposure to ARV using objective indicators such as ARV in plasma or hair, harbouring of resistant viruses, optimal pharmacokinetics and pharmacodynamics parameters, and concomitant use of traditional, complementary and alternative medicines (TCAM) with a view to achieve optimal outcomes from ART. The SA national treatment guidelines do not recommend these for patients entering treatment programmes. So, it is important to begin to understand how these assumptions may impact UTT in SA.

In the absence of a cure or preventive vaccine for HIV, the judicious use of available treatment options in any treatment programme is important and desirable to maximize benefit for patients both at the individual and at the population level, thereby contributing towards a reduction in viral transmission. This narrative aims to examine potential challenges to the expected outcomes of the UTT programme in SA. Specifically, the questions that this review intends to explore are: (1) is there evidence for an appreciable level of drug resistant viruses in HIV infected patients who are not yet on treatment? (2) Are there protocols to identify prior exposure to antiretrovirals for those entering a treatment programme? (3) Is there evidence of archived drug resistant provirus in those who are not yet on treatment? (4) Is there evidence, from more objective markers such as drug concentrations in plasma or hair, to detect the levels of adherence that support a sustained viral load suppression? (5) How does herb–drug interaction impact on the efficacy of ART in SA? Answers to these questions are important in identifying potential challenges, designing interventions, and improving outcomes of UTT in SA.


To narrate whether or not HIV infected persons entering treatment programmes in South Africa do harbour resistant viruses, to such an extent as to reduce the efficacy of the treatment regimen, at least at the population level; and whether pre-treated persons harbour resistant viruses (resistance provirus), published literature in the English language which examined the presence of drug resistant viruses in HIV infected persons who are not on treatment were examined. Literature was sourced in PubMed and Web of Science databases, with no limitation by date of publication. Search terms and criteria implemented in PubMed were: ((HIV[Title/Abstract]) AND "South Africa"[Title/Abstract]) AND resistance [Title/Abstract]; with HIV used as a MeSH Terms.

For the Web of Science, we applied the ‘Advance search’ option for all databases using the following search terms TOPIC: (HIV) OR (HIV-1) OR (Human immunodeficiency virus) OR (Human immunodeficiency virus type 1) AND Topic: (Resistance) AND Topic: (“South Africa”). Additional searches were done to specifically identify data on drug resistant viruses in the drug in-experienced population from each of the nine provinces of South Africa. The outputs of the search of both PubMed and Web of Science were imported into Mendeley and an internal search using the following keys words to generate an output specific for drug resistance studies in the drug inexperienced population: naïve, pre-treatment, in-experienced, and untreated.

Another internal search was done to identify papers on adherence using more objective methods such as drug concentrations in hair and plasma, as well as to identify studies that used proviral DNA to detect HIV drug resistance in the pre-treated population. The search terms used included: resistance, adherence, hair, plasma, and DNA.

Drug pharmacokinetics and pharmacodynamics impact the efficacy of ARV. To estimate the degree of efficiency of ARV pharmacokinetics and pharmacodynamics in the SA population, published data on these topics were reviewed. The search terms and criteria implemented in PubMed were: (((("Human immunodeficiency virus") OR (HIV[Title/Abstract])) OR (HIV-1[Title/Abstract])) AND (((((((MDR1[Title/Abstract]) OR (CYP2B[Title/Abstract])) OR ("Cytochrome p450"[Title/Abstract])) OR ("drug metabolism"[Title/Abstract])) OR ("drug transport"[Title/Abstract])) OR (Pharmacodynamics[Title/Abstract])) OR (pharmacokinetics[Title/Abstract]))) AND ("South Africa"). The MeSH terms included: transportation; metabolic networks and pathways; membrane transport proteins; biological transport; HIV; metabolism; Subheading: metabolism. The terms and criteria for Web of Science, Advance search option included: Web of Science, Advance search option included: TOPIC: (HIV) OR (HIV-1) OR (Human immunodeficiency virus) OR (Human immunodeficiency virus type 1) AND TOPIC: (Drug metabolism and transport) OR (“Cytochrome p450”) OR (CYP2B) OR (MDR1) OR Pharmacodynamics OR Pharmacokinetics AND Topic: (“South Africa”).

The concurrent use of alternative medicine in the form of herbal preparations with ARV may lead to suboptimal adherence and drug interactions with the potential to affect treatment outcome. To estimate the concurrent use of medicinal herbs in South Africa, published data on these topics were also reviewed. The search terms used in both PubMed and Web of Science were; (((("alternative medicine") OR "medicinal plants") OR herb*) AND "Antiretroviral therapy") AND "South Africa" and TOPIC: (alternative medicine) OR (medicinal plants) OR (herb*) AND (Antiretroviral therapy) AND (South Africa) for both PubMed and Web of Science respectively. Figure 1 represents the PRISMA strategies employed in extracting and selecting papers for analysis of resistant viruses in the pre-treated population, pharmacokinetics and pharmacodynamics of ARV, adherence, and use of complementary medicine by individuals on ARV in South Africa.

Fig. 1
figure 1

Prisma flow diagram on the identification and screening of articles for eligibility and inclusion for all search categories. Category 1: searched articles on drug resistant viruses in the pre-treated population in South Africa. Category 2: searched articles on ART adherence using ARV in hair and plasma as a marker. Category 3: searched articles on ARV Pharmacodynamics and Pharmacokinetics. Category 4: search articles on alternative medicinal use by individuals on ARV

Results and discussion

Levels of drug resistant viruses in the pre-treated population

A total of 26 articles met the inclusion criteria and were analyzed for levels of drug resistant viruses in pre-treated patients in South Africa (Fig. 1). The World Health Organization categorizes the levels of HIV drug resistance in a pre-treated population as low (< 5%), moderate (5–15%) and high (> 15%). Our observation shows that there is clear heterogeneity in the prevalence of resistant viruses in the pre-treated population across and within provinces in South Africa. Between 2001 and 2016, 22 studies investigated clinically relevant drug resistance mutations in the pre-treated population at the provincial (n = 18) and national levels (n = 4) (Table 1). The national surveys show a DR mutation prevalence of 0.9–9% falling within the moderate range (Table 1). In almost all the studies, the order of DRM was NNRTI > NRTI > PI with respective prevalence of moderate, low and low (Table 1). Individual studies carried out within the provinces with pregnant mothers (PMTCT) or commercial sex workers as participants show higher levels (> 15%) of DRMs, while all other studies fall within the 5–10% range (Table 1). For example, in a drug resistance threshold survey with samples collected in 2002 and 2004, a prevalence of less than 5% was reported in Gauteng Province in each of the respective years [7]. These mutations included NNRTI (K103N) and NRTI (T69D, K70R). Similarly, analyses conducted at 9 survey sites, five sites in KwaZulu-Natal province and four in Gauteng Province between 2005 and 2009 indicated a low (< 5%) but stable prevalence in Gauteng Province, and a potentially increasing prevalence (5–15%) of NNRTI (K103N, V106M, K101P) mutations in KwaZulu-Natal Province [8]. Protease inhibitor resistance mutations (M46I and I85V) were also low (< 5%) for samples collected in 2007 and 2009 from Gauteng and KwaZulu-Natal respectively [8]. Samples were collected from individuals who met the inclusion criteria for the WHO guidelines for the classification of transmitted drug resistance, and recent infections were selected based on the BED EIA HIV-1 incidence test. Transmitted drug resistance, using Sanger sequences, was significant for Gauteng Province in 2008 (p = 0.009) and in 2009 (p = 0.040), and only for 2009 in Kwazulu-Natal Province (p = 0.029). More recently, Steegen et al. [9] conducted a prospective cross-sectional survey, employing probability proportional to size sampling for drug resistance from each of the nine provinces of SA, between March 2013 and October 2014. Overall, using Sanger generated sequences and analysed by the Stanford Calibrated Population Resistance tool, surveillance drug resistance mutations (SDRM) were detected in 9% (95% CI 6.1–13.0%) of the study population, with a higher prevalence of NNRTI (K103N > Y181C > V106M) followed by NRTI (K65R, M184V) and only 2 PI (V32I and L90M) mutations. Of note is the fact that thymidine analogue mutations (TAMs) were not found in this study population probably due to the switch in 2010 from d4T and AZT to TDF as the backbone of first line therapy. Recently, in estimating the prevalence of provincial and national drug resistance, Hunt and colleagues [10] reported moderate levels (5–15%) of NNRTI transmitted drug resistance in samples collected in 2010 and 2012 from Eastern Cape, Free State and KwaZulu-Natal Provinces. All nine provinces considered, the prevalence of NNRTI was 5.4% (95% CI 3.7–7.8%), with K103N and V106M being the most frequently detected mutations. Estimates for NRTI were 1.1% (95% CI 0.5–2.4%) and 0.6% (95% CI 0.1–1.6%) for PI. Other reports have suggested increasing levels of drug resistance mutations in the pre-treated population in SA. In an analysis of pooled pol sequences between 2000 and 2015, Chimukangara et al. [11] found an increase of reverse transcriptase inhibitor drug resistance mutations from less than 5% prior to 2009 to about 12% in 2015, with at least one-fold increase in mutations conferring resistance to NNRTI and NRTI.

Table1 Summary of provincial and national HIV-1 surveillance drug resistance mutation (SDRM) studies in South Africa

Provincial-based studies have also underlined the heterogeneity in the prevalence of resistance viruses across the national landscape (Fig. 2). Using Sanger sequencing to analyse samples from individuals presenting for voluntary counselling and testing in Limpopo Province in 2008, a less than 5 and 9% of TDR in the Waterberg and Capricorn districts respectively was reported [12]. An examination of samples collected from rural sites between 2000 and 2010 in KwaZulu-Natal found TDR of 6% in 2002 with a decline to < 5% thereafter [13]. Elsewhere in KwaZulu-Natal, resistance threshold studies with samples collected in 2005 and 2009 reported a similar outcome of less than 5% prevalence [14]. However, a recent NGS analysis with a 5% sequence depth threshold documented about 13% viral genetic resistance in a rural population in KwaZulu-Natal Province [15]. This rise in TDR prevalence among drug naïve subjects could be explained partially by the sensitivity of the NGS compared to Sanger sequencing whose lower limit of sensitivity is approximately 20%. Earlier, in a cross-sectional study of reproductive-aged women from 7 clinical sites in Durban (KwaZulu-Natal), Sanger sequencing revealed that 7.4% had NRTI, NNRTI or PI drug resistance mutations prior to treatment initiation [16]. There are indications that the level of resistant viruses is increasing in KwaZulu-Natal Province in a study by Manasa et al. [17] which found no DRM in 2010, 4.7% in 2011 and 7.1% in 2012. Also in KwaZulu-Natal, a prevalence of 9.2% of surveillance drug resistance mutations was noted in individuals less than 15 years of age sampled between 2013 and 2014; and 11% in individuals between 15 and 49 years of age sampled between 2014 and 2015, with no prior exposure to antiretroviral therapy [18].

Fig. 2
figure 2

Map of South Africa showing the highest level of drug resistance in at least one pre-treated population in the provinces. The position of the circles is not indicative of a specific geographical location

In the Free State Province, a prevalence of 2.3% has been reported in adults being prepared for treatment initiation [19]. Among treatment-naive patients, prevalence of primary resistance was 2.5% in Cape Town (Western Cape Province) from sampling done in public sector healthcare facilities between 2002 and 2007 [20]. Similarly, a 3.6% genetic drug resistance was observed in a cross-sectional population from samples collected prior to the commencement of the national treatment programme in Cape Town [21]. On the other hand, drug resistance prevalence of about 27% has been reported in a cross section of drug naïve female sex workers in Soweto, Gauteng province [22]. Provinces in which at least one study has reported a moderate or high level of HIV drug resistance in a pre-treated population are shown in Fig. 2.

Prevention of mother-to-child-transmission (PMTCT)  has been highly successful in SA with only about 3% of children born to HIV infected mothers contracting the infection [23]. Although PMTCT programmes have led to a significant decrease in transmission, the rate of DR among newly infected children remain high (> 15%). For example, a report from a 2011 study performed with samples collected between 2005–2007 among newly infected 2 year olds in Johannesburg who had been exposed to maternal or infant ART, suggests that of those who get infected, as high as 57% harbour viruses resistant to NNRTI, 15% to NRTI and 1.5% to PIs [24]. The prevalence of DRM among PMTCT unexposed children was much lower; NNRTI (24%), NRTI (11%) and PI (1.3%). In a similar study among sdNVP exposed children less than 2 years old who were about to be initiated on ART in Gauteng Province, NNRTI DRM (mostly Y181C) were found to decrease with the child’s age. Notable, up to 68% of 6–12 month olds carried NNRTI mutations while only 16% of NNRTI resistant mutations was detected in children aged 18–24 months old [25]. A study by van Zyl et al. [26] reported a high (16.3%) NNRTI but no NRTI or PI SDRM in children < 18 months old in the Western Cape Province; while another study indicated a moderate prevalence of 11% in children infected vertically [27]. The effectiveness of sdNVP may be compromised by pre-exposure to the same drug. A study by Martinson et al. [28] found that at 6 weeks postpartum, 11.1% of children whose mothers had previously been exposed to sdNVP were HIV-1 positive compared to 4.3% for those without sdNVP exposure. In terms of DRM, 25% of mothers previously exposed to sdNVP carried either K103N or Y181C mutations compared to 12.5 non-exposed mothers who had only Y181C mutations at baseline. In general, PIs have very low DRM among children and have been suggested as a drug of choice for this young population. A study that exclusively examined 2,000 PI naïve sequences collected over a 14 year period detected less than 1% of PI resistance mutations [29]. With the current guideline replacing efavirenz in the standard first line regimen with dolutegravir, it would be of interest to see how this impacts the prevalence of resistance mutations in the pre-treated population. Since most of the studies used the Sanger approach for sequencing, it will be interesting to see if applying the more sensitive next-generation sequencing approach will present a different picture.

Several drivers may be contributing to drug resistance in the pre-treated population. Prevention of mother-to-child transmission programmes have been highly successful; however, this has led to a certain extent of resistance in infants [30]. There is increasing exposure to antiretrovirals through pre-exposure and post-exposure prophylaxes in many African communities [31]. Antiretrovirals are also used in managing pathologies such as HBV [32, 33]; and their use as narcotics has been reported [34]. The detection of drug resistant viruses in non-treated persons is generally assumed to be due to an infection by a resistant virus from an individual on treatment. However, the level of resistance due to de novo changes is not well understood.

Overall, current data show that moderate levels of drug resistance has been reached in certain communities and populations in SA. Sentinel surveillance targeting these communities and populations are required for interventions relevant to local realities.

Prior exposure to ARV and adherence to treatment

It is imperative that the ARV prior exposure history of individuals entering a treatment programme is understood accurately for better management. Generally, there is evidence of self-reporting on prior exposure to ARV, particularly for women of child-bearing age if they have been involved in a PMTCT programme. But there is apparently no evidence of approaches or systems aimed at determining whether any other individual entering an ART programme has been on treatment elsewhere in the absence of a self-report. In particular, males entering a treatment programme are presumed naïve by clinical staff. This may be of concern since, using validated methods, significant amount of drug concentrations has been detected in the hair or plasma of individuals, including males, who self-reported that they have no prior exposure to antiretrovirals upon treatment initiation [35]. The question that arises is whether individuals in whom drug concentrations are detected in hair or plasma are knowingly approaching a different treatment programme to re-initiate. We identified eight studies that reported on the use of hair or plasma as more objective and accurate methods to estimate adherence and sustained drug concentrations in patients (Table 2); [36,37,38,39,40,41]. These approaches could also be employed to detect prior exposure to antiretrovirals. A drawback though is that these methods are expensive, in their requirements for equipment and technical personnel. Therefore, point-of-care devices on these approaches are highly desirable [42].

Table 2 Relevant studies describing the use of hair and plasma as methods to estimate adherence and sustained drug concentrations in patients in South Africa

More objective mechanisms to identify, for example, a patient who approaches the public health sector from the private sector or vice versa, without disclosing prior treatment are necessary. One such method could be the establishment of a centralized national treatment database accessible to authorized healthcare practitioners to support the rationalization of the starting treatment regimen.

Once on treatment, adherence to treatment regimen is fundamental to reduce the chance and speed of drug resistance development, and to achieve outcomes such as undetectable viral load, significant increases in CD4+ T-cells, and reduced morbidity. A study by Myer et al. [43], showed that about 90% of cases with increased viral loads (> 1000 copies µL−1) in a cohort of pregnant women in Cape Town was due to non-adherence to ART as opposed to the presence of pre-treatment drug resistance mutations. Earlier, Hunt et al. [44] in a study in KwaZulu-Natal, identified male gender and individuals entering treatment at an advanced stage of disease as groups for which intensified adherence monitoring may be needed. Perhaps less than optimal adherence is accounting for the currently observed modest level of viral suppression (54%) in the treated population [1]. While data from Hoffmann et al. [45] supports intensified adherence for optimal benefit from first-line use and to minimize the risk for the development of cross-resistance, other studies have advocated for improved adherence to enable patients switching to second line regimens to continue to benefit from treatment [36, 46].

For patients re-initiating treatment, the national treatment guidelines recommend the taking of a thorough history on the previous regimen and duration, noting the reasons for stopping treatment, side effects, and a review of previous viral load data, to inform next steps. However, the guidelines do not emphasize drug resistance testing and mental health assessment. Resistance testing has been shown to be of significant value in managing patients re-initiating treatment even after three months of treatment interruption [47], and more so if deep sequencing is used [48]; and mental health intervention could be critical for improved adherence [49].

Several determinants of non-adherence to ART in the general population or designated populations in South Africa have been proposed. These include psychosocial factors such as stigma, alcohol abuse, and mental health; socio-economic factors such lack of information, unemployment, long distance from clinics; drug toxicities, and deficiencies in the health system such as drug stock-outs and inadequate patient management [50,51,52,53,54]. The significant adversity from non-adherence is highlighted in a model analysis which predicts a spike in HIV infections in South Africa if a decrease in adherence is coupled with an increase in the rate of loss to follow-up in the test and treat programme [55]. There is a myriad of challenges to adherence and an approach whereby the risk of non-adherence for each patient is regularly assessed for the best intervention is the most plausible for an optimal outcome [56].

Host genetics and ARV pharmacokinetics and pharmacodynamics

The variability in host genetics impacts pharmacokinetics and pharmacodynamics of ARV. This, in turn may lead to differences in plasma drug concentration, which can impact drug resistance development or adverse effects [57, 58]. Our search revealed only seven (n = 7) articles relevant to the relationship between host genetics of the ethnically diverse South African population and pharmacokinetics and pharmacodynamics of ARV (Table 3). In a study to determine the frequency of genetic variants associated with the pharmacokinetics of ARV in South Africans, there were differences in 53 variants in allele and genotype frequencies between designated South African coloureds and South African Blacks; with 24.5% of these being of clinical relevance impacting on the pharmacokinetics of tenofovir and efavirenz [59]. Similarly, a study by Swart and colleagues [60] reported differences in allele frequencies among South Africans with African, Caucasian and Asian populations. Specifically, they reported that CYP2B6 516 T and 785G (*6) and CYP2B6 983C (*18) alleles are significantly associated with high plasma efavirenz levels, and G-G-A-T-C and A-G-A-T-C haplotypes showed significantly lower levels of efavirenz. The authors suggest screening for CYP2B6 516G > T SNP, which has a high specificity and positive predictive value, as a marker for efavirenz dosage for individuals homozygous for the variant. In another study, polymorphisms in the CYP2B6 gene associated with extensive, intermediate and slow metabolizers of efavirenz were identified in black adults and children [61]. Studies by Reay and colleagues also suggest that CYP2B6 haplotypes could be used to predict EFV plasma concentrations in black South African children [62, 63]. These findings though should be seen in the backdrop of recent changes (November 2019) in which, dolutegravir replaced efavirenz because of the former’s high genetic resistance barrier. It is expected that data on the relationship of dolutegravir and host genetics will soon begin to emerge. Examination of the MDRI gene among black South Africans did not find significant difference between immune recovery and decline in viral load with MDRI genotypes [64]. Similarly, a univariate two-way analysis of variance did not show any effect of ethnicity on CD4+ T-cell count in response to ART, but T129C and G2677A polymorphisms in the ABCB1 gene showed a marginal effect on immune recovery [65].

Table 3 Relevant studies on the relationship between host genetics and ARV transport and metabolism in the ethnically diverse South African population

Overall, there is limited data to guide our understanding, to an appreciable extent, on the relationship between host genetics of the ethnically diverse South African population and pharmacokinetics and pharmacodynamics of ARV, and how this impacts viral suppression in the era of test and treat.

Traditional, complementary and alternative medicine, adherence to ARV, and treatment outcome

The use of traditional, complementary and alternative medicine (TCAM) in the treatment of HIV infection is quite common in low- and middle-income countries, including South Africa, and herbal preparations are taken concurrently with ARV [66,67,68,69]. Since optimal adherence to ART is key to sustained viral suppression, the concern has been how the concomitant use of TCAM impacts adherence to ART. We extracted 20 studies relevant to the association between the use of TCAM and ART outcomes in South Africa (Table 4). Earlier, a multivariate regression analysis involving participants from public sector hospitals in KwaZulu-Natal showed that although TCAM usage reduced in the study population upon initiation into ART, adherence to ART was significantly impacted among those who continued using TCAM [70].This observation was further emphasized in a prospective study [71]; in which the use of TCAM was observed to be significantly associated with virologic failure [72]. Understanding the use of TCAM during counselling sessions on adherence to ART can be missed since patients may withhold this information from healthcare workers for fear of lack of understanding and disclosure of their HIV positive status to close relatives and friends [73, 74]. A study by Loeliger and others [75] proposed a strengthening of collaboration between community health care providers and traditional healthcare providers as a means of sustaining adherence to ART; and a recent study has shown that adherence to ART could be improved even among HIV infected traditional healthcare practitioners [76].

Table 4 Studies reviewed on the association between the use of alternative medicines and ART outcomes in South Africa

Another concern is whether there are interactions between phytochemicals from herbal preparations and ARV and whether or not these negatively impacts the efficacy of ARV, or how do phytochemicals interact indirectly with ARV pharmacokinetics and pharmacodynamics. For example, the powder derived from the leaf of Moringa oleifera Lam., has been shown to inhibit cytochrome p450 3A4, 1A2 and 2D6 activity in-vitro, but its intake did not alter the pharmacokinetics of nevirapine in a one-sequence cross over study of HIV patients on ART [77]. Members of Hypoxis hemerocallidea, known as African potato and commonly used as an alternative in the management of AIDS, potentially inhibit the efflux of indinavir, a protease inhibitor, across human cell lines, and increases indinavir bioavailability in Sprague–Dawley rats [78]. Equally, Sutherlandia frutescens has been reported to significantly reduce the availability of another protease inhibitor, atazanavir, in-vitro [79]. The transport of atazanavir is mediated by P-glycoprotein (P-gp), while its metabolism is mediated by CYP3A4 and CYP3A5. Studies have advocated for clinical trials to better understand the herbal-drug interactions with the aim of improving the outcomes of ARV use in South Africa [80, 81]. It is plausible that herb–drug interactions could negatively impact ARV bioavailability and lead to the development of drug resistance and poor viral suppression. Nevertheless, our improved understanding may be hindered by the evolving changes in treatment regimens.

The development of acquired resistance is almost inevitable in antiretroviral therapy and plays a major role in viral suppression [82]. Acquired resistance is also driven by several factors, sometimes intertwined, and efforts aimed at minimizing the development of acquired resistance at the individual and at the population levels are in different fronts. These include, administering the best treatment regimen to provide the highest genetic barrier for viral resistance, maintaining optimal adherence through counselling and programmatic measures, and regular viral load monitoring for timely interventions.

The outcomes described here-in should be appreciated in the background of some limitations. First, psychosocial parameters, such as mental health, stigma and discrimination, which are potential challenges to the desired outcomes of the 90-90-90 strategy were not reviewed for impact. For example, stigma, discrimination and depression may impede willingness for voluntary counselling and testing, and also negatively impact adherence and favourable viral suppression for those on treatment. Second, drug stock-outs and drug toxicities were not reviewed for implications in non-adherence, although allusions on how they impact adherence have been indicated. Third, although we attempted to make this review as complete as possible, the literature search did not include abstracts and conference proceedings, or data presented in theses and dissertations.


This narrative review provides insights on antiretroviral drug resistance in the pre-treated population in South Africa which has reached moderate levels in specific geographic populations in eight of the nine Provinces. Therefore, in the face of the expanding access to treatment, the implementation of Provincial drug resistance surveillance systems to track trends for intervention is a reasonable proposal to support efforts and sustain the goals of UTT considering local realities. There is copious data showing that TCAM is commonly used concomitantly with ARV resulting in negative impacts on ARV adherence and viral suppression. It is therefore important that TCAM should be taken seriously during counselling sessions for patients on ARV as it directly relates to achieving viral suppression. Also, there should be more objective and coordinated approaches to detect prior exposure to ARV before treatment initiation. In certain populations such as children, pregnant women, males, and those who have been on ART for more than three years, adherence measures should be intensified. On the other hand, there is a dearth of data on host genetics of the very diverse ethnic South African population and its relationship to bioavailability of antiretrovirals. Future investigations should also provide additional data for empirical evidence that the population is accessing the test and treat programme early enough, and that adherence is sustained. Definitely, resistance in the pre-treated population, TCAM, adherence, host genetics are all interwoven on their impact on viral suppression (Fig. 3). Optimal management of these relationships will be beneficial in ensuring sustained viral suppression in at least 90% of those on treatment, a key component of the 90-90-90 strategy.

Fig. 3
figure 3

South Africa’s progress towards the 90-90-90 target (, 2019) and factors associated with unsuppressed HIV viral loads