Quantifying the impact of nonadherence patterns on exposure to oral immunosuppressants

Leveraging mathematical modeling to analyze nonadherence for hydroxyurea therapy in sickle cell disease

Nonadherence is common in individuals with sickle cell disease (SCD) on hydroxyurea therapy and can be observed with waning improvements in hematologic parameters or biomarkers like mean cell volume and fetal hemoglobin level over time. We modeled the impact of hydroxyurea nonadherence on longitudinal biomarker profiles. We estimated the potential nonadherent days in individuals exhibiting a drop in biomarker levels by modifying the dosing profile using a probabilistic approach. Incorporating additional nonadherence using our approach besides existing ones in the dosing profile improves the model fits. We also studied how different patterns in adherence give rise to various physiological profiles of biomarkers. The key finding is consecutive days of nonadherence are less favorable than when nonadherence is interspersed. These findings improve our understanding of nonadherence and how appropriate intervention strategies can be applied for individuals with SCD susceptible to the severe impacts of nonadherence.

Effect of Missed Doses on the Therapeutic Effect of Methotrexate for Rheumatoid Arthritis: A Pharmacokinetic Modeling Study

Introduction

Patients rarely, if ever, take their medications exactly as prescribed. The extent to which missed doses interfere with a drug's therapeutic effect remains unclear.

Methods

After weekly oral dosing of methotrexate (MTX) for rheumatoid arthritis, its polyglutamate derivatives (MTXglu) accumulate in red blood cells, where they are markers for the drug's therapeutic effectiveness. We used Medication Event Monitoring System data and pharmacokinetic modeling to analyze whether missing MTX doses causes the MTXglu level in red blood cells to fall below the range associated with the drug's clinical effect.

Results

For patients initiating oral MTX, the threshold for clinical effectiveness and the steady state level were reached in medians of 6 weeks and 22 weeks, respectively. For patients at steady state who discontinued MTX, the MTXglu level fell below the therapeutic threshold after a median of 3 weeks. After initiating MTX, single missed doses did not cause a loss of therapeutic effect in the median patient if they occurred after 10 weeks, while runs of ≥3 consecutive missed doses did cause the MTXglu level to fall below the therapeutic threshold.

Conclusion

While there is considerable variation between patients, pharmacokinetic modeling indicates that instances of isolated single missed doses of MTX typically will not cause polyglutamated methotrexate levels in red blood cells to fall below the range associated with the therapeutic effect. Runs of ≥3 consecutive missed doses, however, are typically expected to result in a loss of the therapeutic effect.

Patterns of non-adherence to oral antiretroviral medication: frequencies of consecutively missed doses

PURPOSE

The therapeutic effect of a once-daily oral drug will be maintained if there are no occurrences of consecutively missed doses that exceed the duration of the drug's effect. The durations of effect of antiretroviral drugs are typically in the range of 1-4 days. Here, we report the observed frequencies of ≥2, ≥3, and ≥4 consecutively missed doses for patients taking a once-daily oral antiretroviral drug for HIV infection.

PATIENTS AND METHODS

Medication Event Monitoring System (MEMS) data were extracted from an electronic database of MEMS records, for a 30-day period for 555 patients taking once-daily oral HIV drug therapy. We recorded the number of days with missed doses and occurrences of ≥2, ≥3, or ≥4 consecutively missed doses. Distributions of the observed frequencies of ≥2, ≥3, and ≥4 consecutively missed doses as a proportion of number of missed doses were compared to calculated random distributions using the Wilcoxon signed-rank test.

RESULTS

The frequencies of 0, 1, and ≥2 missed daily doses were 0.279, 0.312, and 0.409, respectively. The frequencies of ≥2, ≥3, and ≥4 consecutively missed doses were 0.184, 0.110, and 0.065, respectively. The probabilities that the observed frequencies of ≥2, ≥3, and ≥4 consecutively missed doses were as expected from random chance were P=0.345, P<0.01, and P<0.01, respectively.

CONCLUSION

Observed runs of ≥3 and ≥4 consecutively missed doses - and hence loss of therapeutic effect for drugs of duration of action of <3 and <4 days, respectively - occurred more frequently than expected if missed doses were randomly distributed.

Acceptable medication non-adherence: A crowdsourcing study among French physicians for commonly prescribed medications

Background

Achieving good medication adherence is a major challenge for patients with chronic conditions. Our study aimed to assess the Threshold for Unacceptable Risk of Non-adherence (TURN), defined as the threshold at which physicians consider the health risks incurred by patients due to medication non-adherence unacceptable, for the most commonly prescribed drugs in France.

Methods

We conducted an online study using a crowdsourcing approach among French general practitioners and specialists from September 2016 to August 2017. Physicians assessed the TURN for various levels of missed doses by evaluating a series of randomly presented clinical vignettes, each presenting a given medication with a given therapeutic indication. For each “drug-indication group” (i.e., all drugs from the same pharmacological class with a similar therapeutic indication): 1) we described the distribution of physicians’ assessments, 2) we provided a summary estimate of the TURN, defined as the frequency of missed doses above which 75% of the physicians’ assessments were located; 3) we computed the number of pill boxes reimbursed in France in 2016 to put our results into context.

Results

We collected a total of 5365 assessments from 544 physicians, each of whom evaluated a random sample among 528 distinct clinical vignettes. Estimates of the TURN varied widely across drug-indication groups, ranging from risk considered unacceptable with 1 daily dose missed per month (e.g., insulin for diabetes) to risk always considered acceptable (e.g., anti-dementia drugs). Drugs with an estimated TURN of over one missing daily dose per week represented 44.9% of the prescription volume of the medications assessed in our study.

Conclusions

According to physicians, the impact of non-adherence may vary greatly. Patient-physician discussions on the variable consequences of non-adherence could lead to a paradigm shift by seeking to reach “optimal adherence” depending on drugs rather than unrealistic “perfect adherence” to all drugs.

The Adherence Rate Threshold is Drug Specific

Introduction

Patient adherence to a medication regimen is usually expressed as an adherence rate, defined as the proportion of prescribed doses actually taken. An adherence rate threshold, above which the therapeutic effect is maintained, is typically assigned an arbitrary value, commonly 0.8.

Objective

Here, we determined the value of the adherence rate threshold objectively in different drugs of the same class, using statins as an example.

Methods

We used pharmacokinetic/pharmacodynamic (PK/PD) modeling to predict serum levels of low-density lipoprotein cholesterol (LDL-C) in patients taking simvastatin 20 mg or atorvastatin 5 mg once daily for 30 days. LDL-C reduction was modeled for adherence rates of 1.0, 0.8, 0.6, 0.4, and 0.2. The results were expressed as the percentage of time spent at the LDL-C goal (< 70 mg/dL). The adherence rate threshold was defined as the minimum adherence rate that resulted in the same amount of time at goal as perfect adherence (i.e., a rate of 1.0).

Results

For simvastatin, an adherence rate of 0.8 resulted in a significant decrease in time at the LDL-C goal compared to perfect adherence (54.8% versus 85.1%; P < 0.001), and rates < 0.8 resulted in progressively less time at goal. For atorvastatin, the rates of 0.8 and 0.6 resulted in essentially the same amount of time at goal as perfect adherence (87.8% and 87.7%, respectively, versus 88.1%; P > 0.05 for both), with less time at goal only occurring at rates ≤ 0.4 (P < 0.001). Thus, the adherence rate thresholds are > 0.8 for simvastatin and between 0.4 and 0.6 for atorvastatin.

Conclusion

These results indicate that a value of 0.8 cannot be applied universally.

Relationship Between Adherence Rate Threshold and Drug 'Forgiveness'

The medication adherence rate (A) is the proportion of prescribed drug doses consumed within a given time period. It is often assumed that there is an adherence rate threshold (A _th) at or above which the therapeutic effect of the medication is maintained. Drug forgiveness (F) is the number of consecutive doses that can be missed while still maintaining a therapeutic effect. At a given value for A, the therapeutic effect of the drug will be continuously maintained if there is no possibility of >F missed doses. Hence, for a once-daily drug prescribed for N days, A _th and F are related by the formula, A _th = (N - F)/N. At adherence rates below A _th the probability of maintaining the therapeutic effect is equal to the probability of there being no instances of >F consecutive missed doses. Since F is a function of the duration of the drug effect (D) and D varies depending on the specific drug's pharmacokinetic/pharmacodynamic properties, there is no universal A _th applicable to all drugs.

Posttransplant Medical Adherence: What Have We Learned and Can We Do Better?

Purpose of review

Non-adherence to the medical regimen after kidney transplantation can contribute to poor clinical outcomes, and strategies to maximize adherence are sought by care providers and patients alike. We assessed recent evidence on prevalence, risk factors, and clinical outcomes associated with non-adherence to the medical regimen after kidney transplantation. We summarized recent clinical trials testing interventions to improve adherence and generated recommendations for future research and clinical practice.

Recent findings

A large evidence base documents rates of non-adherence to each of the multiple components of the regimen, including medication-taking, lifestyle activities, clinical care requirements, and substance use restrictions. Some risk factors for non-adherence are well known but the full range of risk factors remains unclear. Non-adherence to immunosuppressants and to other components of the regimen increases morbidity and mortality risks. Recent interventions, including education and counseling; electronic health strategies; and medication dose modifications, show promise for reducing immunosuppressant non-adherence. However, most of these interventions would be difficult to deploy in everyday clinical practice. Systematic dissemination of efficacious interventions into clinical practice has not been undertaken.

Summary

Rates and risk factors for non-adherence to the medical regimen have been examined and there is evidence that non-adherence may be ameliorated by a range of interventions. Although gaps in the evidence base remain, it would be timely to devote greater efforts to dissemination of findings. Thus, efforts are needed to assist transplant programs in using existing evidence to better identify patients who are non-adherent and to design and implement strategies to reduce or prevent non-adherence.

The effect of medication nonadherence on progression-free survival among patients with renal cell carcinoma

OBJECTIVE

To examine how observed medication nonadherence to 2 second-line, oral anticancer medications (axitinib and everolimus) affects progression-free survival (PFS) among patients with renal cell carcinoma.

METHODS

We used an adherence-exposure-outcome model to simulate the impact of adherence on PFS. Using a pharmacokinetic/pharmacodynamic (PK/PD) population model, we simulated drug exposure measured by area under the plasma concentration-time curve (AUC) and minimum blood or trough concentration (Cmin) under 2 scenarios: 1) optimal adherence and 2) real-world adherence. Real-world adherence was measured using the medication possession ratios as calculated from health insurance claims data. A population PK/PD model was simulated on individuals drawn from the Medical Expenditure Panel Survey (MEPS), a large survey broadly representative of the US population. Finally, we used previously published PK/PD models to estimate the effect of drug exposure (i.e., Cmin and AUC) on PFS outcomes under optimal and real-world adherence scenarios.

RESULTS

Average adherence measured using medication possession ratios was 76%. After applying our simulation model to 2164 individuals in MEPS, drug exposure was significantly higher among adherent patients compared with nonadherent patients for axitinib (AUC: 249.5 vs. 159.8 ng×h/mL, P<0.001) and everolimus (AUC: 185.4 vs. 118.0 µg×h/L, P<0.001). Patient nonadherence in the real world decreased the expected PFS from an optimally adherent population by 29% for axitinib (8.4 months with optimal adherence vs. 6.0 months using real-world adherence, P<0.001) and by 5% (5.5 vs. 5.2 months, P<0.001) for everolimus.

CONCLUSION

Nonadherence by renal cell carcinoma patients to second-line oral therapies significantly decreased the expected PFS.

Patient survey to identify reasons for non-adherence and elicitation of quality of life concepts associated with immunosuppressant therapy in kidney transplant recipients

BACKGROUND

Renal transplantation (RT) is considered the treatment of choice for end-stage renal disease compared to dialysis, offering better health-related quality of life (HRQoL) and higher survival rates. However, immunosuppressants are essential for the long-term survival of kidney grafts and patients' non-adherence to their medication leads to poor outcomes. Immunosuppressants can also significantly alter patients' HRQoL because of their side effects and the complex chronic medication regimen they represent.

PURPOSE

To elicit key concepts related to adherence to immunosuppressant therapy (IT) and reasons for non-adherence in terms of patient reported outcomes, side effects, and the impact of the medication on HRQoL in RT population, including patient preference of once daily over twice-daily immunosuppressive regimen. Results were used to develop an IT-specific conceptual framework and provide suggestions for improving patients' adherence to IT.

MATERIALS AND METHODS

Interviews were conducted with three clinical experts to determine key concepts related to RT and immunosuppressants. Thirty-seven participants in four focus groups were asked to cite important concepts related to adherence and impact of IT on HRQoL and to rate them. Qualitative analysis was conducted to code participants' responses.

RESULTS

Non-adherence among participants where admitted was unintentional. The reason for this included forgetfulness, interference with lifestyle, being asleep at the time the medication should be taken, change in routine, and impact of side effects. Overall, participants reported that the evening dose was more problematic to remember and that the exclusion of this dose could make them more adherent. Participants also reported that IT impacted on their HRQoL in a number of ways including: placing restrictions on their lifestyle, causing anxiety, or impairing their ability to work.

CONCLUSION

This study provides qualitative evidence about the barriers to IT adherence and the components of HRQoL that are important from the perspective of RT patients. The developed conceptual framework of IT-HRQoL in RT transplants, including social, psychological, and work life domains, can be used to inform the development of a new IT-specific measure of HRQoL in RT patients for use in head-to-head clinical trials or observational studies. Despite limitations associated with the number and the age range of patients recruited, this study suggests that a change in the regimen from twice-daily to once daily among other measures could improve their adherence to IT and their HRQoL by placing less restrictions on their lifestyles.

How to handle missed or delayed doses of tacrolimus in renal transplant recipients? A pharmacokinetic investigation

Every transplant patient will, at least occasionally, miss immunosuppressive drug doses or take them outside the prescribed times. This study aims at quantifying the impact of poor execution on tacrolimus exposure in renal transplant patients. Validated pharmacokinetic tools applied in clinical setting were used to simulate the steady-state pharmacokinetic profiles of the drug when given as the immediate-release formulation to renal transplant patients, being CYP3A5 expressors or not, and who have reached either a standard or a minimized exposure. Situations of interruption due to a missed or delayed dose were simulated and the impact on drug exposure was explored. In case of a missed dose, it was observed that: (i) a single forgotten dose can greatly impact exposure: up to 49% decrease for tacrolimus trough concentration and 70% for AUC0-12 h in patients with the highest clearance values; (ii) patients with a minimized exposure are the most affected by a missed dose; and (iii) a dose of 1.5 times the usual dose may be recommended after a total dose oversight. Considering that intra-patient exposure variability is a predictive factor of poor graft outcome, these modeling results may serve as recommendations for patients, both preventively and in response to their questions.

Defining medication adherence in individual patients

BACKGROUND

The classification of patients as adherent or non-adherent to medications is typically based on an arbitrary threshold for the proportion of prescribed doses taken. Here, we define a patient as pharmacokinetically adherent if the serum drug levels resulting from his/her pattern of medication-taking behavior remained within the therapeutic range.

METHODS

We used pharmacokinetic modeling to calculate serum drug levels in patients whose patterns of dosing were recorded by a medication event monitoring system. Medication event monitoring system data were from a previously published study of seven psoriasis patients prescribed 40 mg subcutaneous adalimumab at 14-day intervals for 1 year. Daily serum concentrations of adalimumab were calculated and compared with a known therapeutic threshold.

RESULTS

None of the seven patients took adalimumab precisely every 14 days. Three patients who took adalimumab at intervals of 6-26 days could be classified as pharmacokinetically adherent, because their daily adalimumab serum concentration never fell below the therapeutic threshold. The four other patients, who took adalimumab at intervals of 7-93 days, could be classified as pharmacokinetically non-adherent, because their adalimumab serum concentration fell below the therapeutic threshold on 3.5%-71.3% of days.

CONCLUSION

Patients with varying patterns of adalimumab dosing could be classified as pharmacokinetically adherent or non-adherent according to whether or not their serum drug concentrations remained within the therapeutic range.

Impact of tacrolimus intraindividual variability and CYP3A5 genetic polymorphism on acute rejection in kidney transplantation

BACKGROUND

Wide variation in tacrolimus concentrations and low tacrolimus exposure have been reported to be associated with poor renal graft outcomes in non-Asians. The CYP3A5 polymorphism is a representative genetic factor that might affect this association, together with environmental factors. We investigated whether tacrolimus variability or the mean tacrolimus trough concentration can influence kidney allograft outcomes in Asians and whether the CYP3A5 polymorphism (rs776746) can affect this relationship.

METHODS

Data from renal transplant patients between 2000 and 2010 were analyzed retrospectively. The tacrolimus intraindividual variability (IIV) and the mean tacrolimus trough concentration were calculated from the tacrolimus concentrations between 6 and 12 months after transplantation.

RESULTS

A total of 249 renal transplant patients were enrolled. The patients with higher tacrolimus IIV had shorter rejection-free survival (P = 0.002). However, there was no difference in rejection-free survival between CYP3A5 expressers and nonexpressers. The tacrolimus IIV was not associated with the CYP3A5 polymorphism. High IIV of tacrolimus was an independent risk factor of biopsy-proven acute rejection after adjusting for mean tacrolimus concentration, HLA mismatch, induction therapy, donor type, and CYP3A5 polymorphism (hazard ratio 2.655, 95% confidence interval 1.394-5.056). Interestingly, the impact of tacrolimus IIV on acute rejection was significant in CYP3A5 expressers, whereas it was not in CYP3A5 nonexpressers.

CONCLUSIONS

The IIV of tacrolimus trough concentrations had a significant impact on rejection-free survival. The effect was influenced by CYP3A5 polymorphism, although the tacrolimus variability itself was not determined by the CYP3A5 polymorphism.