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Record W2754128718 · doi:10.1097/qad.0000000000001617

Dolutegravir and metformin

2017· article· pl· W2754128718 on OpenAlexaff
Mark Naccarato, Deborah Yoong, I. W. Fong

Bibliographic record

VenueAIDS · 2017
Typearticle
Languagepl
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsUniversity of TorontoKingston Process Metallurgy (Canada)St. Michael's Hospital
FundersGilead Sciences
KeywordsMetforminDolutegravirMedicineLactic acidosisPharmacokineticsHypoglycemiaPharmacologyInternal medicineEndocrinologyGastroenterologyInsulinImmunologyViral load

Abstract

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Metformin is an antihyperglycemic considered to have a wide therapeutic window, while having a negligible risk for causing hypoglycemia [1]. It is commonly associated with gastrointestinal intolerance and can cause weight loss in some patients [1,2]. Although rare, its most serious toxicity is lactic acidosis, which has been associated with mortality rates of 30–50% and is often seen when metformin serum concentrations are elevated [1]. Following gut absorption, 90% of metformin is excreted unchanged in the urine [3], both by passive filtration as well as active tubular secretion utilizing organic cation transporter 2 (OCT2) for tubular uptake along with multidrug and toxin extrusion protein (MATE)1, and multidrug and toxin extrusion 2-K for final secretion into the urine [3]. Thus, any mechanism that may interfere with metformin's clearance may increase the risk of lactic acidosis. Dolutegravir is an antiretroviral, which precipitates few drug–drug interactions because it does not significantly modulate the cytochrome P450 or glucuronidation systems; however, dolutegravir does inhibit the OCT2 transporter [3]. A pharmacokinetic study in healthy volunteers demonstrated that the addition of dolutegravir 50 mg daily and 50 mg twice daily increased the area-under-the-curve of metformin by 79 and 145%, respectively [3]. A recent observational study by Gervasoni et al.[4] provided their clinical experience with 15 HIV-infected patients receiving concurrent dolutegravir and metformin and reported no significant change in mean fasting blood glucose, hemoglobin A1c, and no patient experienced episodes of hypoglycemia or lactic acidosis after the initiation of dolutegravir. However, it is uncertain what dosage was used as well as the level of adherence to metformin therapy in this cohort. Gervasoni et al. concluded that the clinical relevance of this interaction is limited [4]. We report the first case of hyperlactatemia in a patient receiving both dolutegravir and metformin. A 77-year-old HIV-infected woman presented to our clinic in April 2017 with severe generalized lipoatrophy and weight loss over the past 2 years. Her latest CD4+ cell count and HIV-1 RNA were 806 cells/μl and less than 40 copies/ml, respectively. Her past medical history included diabetes, hypertension, sarcoidosis, osteoporosis, chronic back pain, and asthma. Her medications at this time consisted of dolutegravir/abacavir/lamivudine, metformin/sitagliptin, amlodipine, atorvastatin, bisoprolol, alendronate, oxycodone/naloxone, and vitamin D. Suspecting her medications were contributing to her symptoms, we changed her antiretroviral regimen to a nucleoside-sparing combination of dolutegravir 50 mg daily and rilpivirine 25 mg daily and her metformin/sitagliptin was held until further investigations were completed. Her venous lactate and corresponding medication changes are presented in Table 1.Table 1: Venous lactate measurements while on concurrent dolutegravir and metformin and after discontinuing dolutegravir.Given the timeline of events and ruling out other causes of lactic acidosis, we hypothesize that dolutegravir led to elevated metformin concentrations via inhibition of OCT2 resulting in hyperlactatemia. Metformin accumulation is usually observed in settings of renal failure, whereas impaired hepatic metabolism of lactate along with conditions that increase production may contribute to lactic acidosis [1]. Although our patient may be considered to have mild renal impairment given her advanced age and lack of muscle mass, simply removing the interaction (i.e. dolutegravir) led to a normalization of her serum lactate while remaining on metformin. There have been several such case reports where cimetidine, another OCT2 inhibitor, may have precipitated an interaction with metformin resulting in lactic acidosis [5–7]. Limitations include that we were unable to measure metformin serum concentrations because of lack of an assay at our institution, as well as the simultaneous discontinuation of her nucleoside backbone. Nucleoside reverse transcriptase inhibitors (NRTIs) are associated with mitochondrial dysfunction and all have been implicated in cases of lactic acidosis. However, cases of lactic acidosis are most often associated with the older nucleoside agents, such as stavudine and didanosine [8,9]. In fact, cases of lactic acidosis are so rare with the newer NRTIs that some authors have suggested switching patients with asymptomatic NRTI-associated hyperlactatemia to agents with minimal risk, such as abacavir, lamivudine, and tenofovir [9]. Also noteworthy to the clinician, is that the resolution of NRTI-associated hyperlactatemia is often a slow process requiring mitochondrial DNA concentrations to be replenished prior to the normalization of lactate concentrations. In one case series, it took 4–28 weeks after the discontinuation of NRTIs for lactate levels to return to normal [10] unlike the rapid resolution that was observed over a few days in our case. Despite the above limitations, when applying the drug interaction probability scale, our case rates as a probable drug–drug interaction [11]. Therefore, we suggest that physicians remain vigilant in monitoring for toxicity and limit the total daily dose of metformin to 1000 mg when metformin is taken concomitantly with dolutegravir [12], and would suggest the avoidance of this combination in patients at highest risk for metformin-associated lactic acidosis, such as elderly patients with suboptimal renal function. Acknowledgements Conflicts of interest There are no conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.307
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2017
Admission routes1
Has abstractyes

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