Treatments for Moderate-to-Severe Alopecia Areata: A Systematic Narrative Review.

Treatments for alopecia areata (AA) have traditionally been prescribed off-label, and there has been no universal agreement on how to best manage the condition. Baricitinib is the first oral selective Janus kinase (JAK) inhibitor approved for the treatment of adults with severe AA. As a better understanding of the evidence supporting the management of AA in clinical practice is needed, we conducted a systematic literature review and subsequent narrative review to describe available evidence pertaining to the efficacy and tolerability of treatments currently recommended for adults with moderate-to-severe forms of AA. From 2557 identified records, a total of 53 records were retained for data extraction: 9 reported data from 7 randomized controlled trials (RCTs) versus placebo, and 44 reported data from unique RCTs with no placebo arm, non-randomized trials, or observational studies. Across drug classes, data were reported heterogeneously, with little consistency of data collection or clinical endpoints used. The most robust evidence was for the JAK inhibitor class, in particular the JAK1/JAK2 inhibitor baricitinib. Five RCTs (three for baricitinib) demonstrated a consistent benefit of JAK inhibitor therapy over placebo across various clinical outcomes in adult patients with at least 50% scalp hair loss. Overall, hair regrowth varied widely for the other drug classes and was generally low for patients with moderate-to-severe AA. Relapses were commonly observed during treatment and upon discontinuation. Adverse effects were generally consistent with the known safety profile of each intervention. The heterogeneity observed prevented the conduct of a network meta-analysis or an indirect comparison of different treatments. We found that the current management of patients with moderate-to-severe AA often relies on the use of treatments that have not been well evaluated in clinical trials. The most robust evidence identified supported the use of baricitinib, and other oral JAK inhibitors, in patients with severe AA.

To date, there has been no universal agreement on how to best manage alopecia areata (AA), suggesting that a better understanding of the evidence for the different treatment options is needed. Most of the treatments traditionally used for AA have not been approved for this indication. Baricitinib is the first oral selective Janus kinase (JAK) inhibitor approved for the treatment of adults with severe AA. Consequently, we extensively reviewed the available literature for evidence regarding the efficacy and tolerability of treatments currently recommended for adults with moderate-to-severe forms of AA. Although we found many potential reports, only 53 provided the type of information we believed to be relevant, with 9 describing findings from 7 randomized controlled trials versus placebo. Across treatments, there was little consistency of data collection or clinical endpoints used. The most robust evidence was for the JAK inhibitor class, in particular baricitinib, which was consistently more beneficial than placebo across various clinical outcomes in adults with at least 50% scalp hair loss. For the other classes of drugs, hair regrowth varied widely, was generally low for patients with moderate-to-severe hair loss, and commonly did not last. Reported adverse effects were generally as expected for each treatment. We found that the current management of patients with moderate-to-severe AA often relies on the use of treatments that have not been well evaluated in clinical trials. However, strong evidence supports the use of baricitinib, and other oral JAK inhibitors, in patients with severe AA.

Clinical Outcomes for Uptitration of Baricitinib Therapy in Patients With Severe Alopecia Areata: A Pooled Analysis of the BRAVE-AA1 and BRAVE-AA2 Trials.

Importance: Baricitinib is an oral selective Janus kinase 1/2 inhibitor that has achieved clinically meaningful outcomes for scalp, eyebrow, and eyelash hair regrowth in patients with severe alopecia areata (AA) at week 36 of treatment. Treatment with baricitinib, 4 mg, has resulted in higher response rates than baricitinib, 2 mg, at weeks 36 and 52. Objective: To determine the efficacy of uptitration to baricitinib, 4 mg, for 24 weeks in patients who had previously not responded to baricitinib, 2 mg (Severity of Alopecia Tool [SALT] score of >20). Design, Setting, and Participants: BRAVE-AA1 and BRAVE-AA2 are multicenter, placebo-controlled, phase 3 randomized clinical trials that were initiated on September 24, 2018, and July 8, 2019, respectively, with follow-up to 200 weeks (data cutoffs of November 11, 2021, and November 5, 2021, respectively). This pooled analysis reports long-term extension data up to week 76. At baseline, 1200 adult patients with severe AA (SALT score ≥50) were randomly assigned in a 3:2:2 ratio to receive baricitinib, 4 mg; baricitinib, 2 mg; or placebo. Patients treated with baricitinib remained on the same treatment dose until week 52. Patients were considered nonresponders to baricitinib, 2 mg, if they had a SALT score greater than 20 after 52 weeks of therapy. Main Outcomes and Measures: The proportions of patients achieving a SALT score of 20 or lower and clinician-reported outcome for eyebrow hair loss and eyelash hair loss scores of 0 or 1 (full coverage or minimal gaps) with 2-point or higher improvements from baseline (among those with baseline scores ≥2 [significant gaps to no notable hair]) were analyzed through week 76. Results: At week 52, of the 340 patients (mean [SD] age, 38.4 [12.9] years; 212 [62.4%] female) treated with baricitinib, 2 mg, 212 (62.4%) had a SALT score higher than 20 and were uptitrated to baricitinib, 4 mg. Two-thirds of these patients (142 of 212 [67.0%]) had a baseline SALT score of 95 to 100, indicating very severe AA. At week 76, 55 of the 212 patients (25.9%) had achieved a SALT score of 20 or lower. During the same period, response rates for clinician-reported outcome scores of 0 or 1 increased from 19.3% (31 of 161 patients) to 37.9% (61 of 161 patients) for eyebrows and from 24.1% (33 of 137 patients) to 40.9% (56 of 137 patients) for eyelashes. Conclusions and Relevance: In this pooled analysis of the BRAVE-AA1 and BRAVE-AA2 trials, uptitration of baricitinib, 2 mg, to baricitinib, 4 mg, in those who did not respond to the 2-mg dose resulted in meaningful improvement of response rates over the subsequent 24 weeks for scalp, eyebrow, and eyelash hair loss. Trial Registration: ClinicalTrials.gov Identifiers: NCT03570749 and NCT03899259.

Predicting disease genes based on multi-head attention fusion.

BACKGROUND: The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes. RESULTS: This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association. CONCLUSIONS: The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction.

Efficacy and Safety of Baricitinib in Patients with Severe Alopecia Areata over 52 Weeks of Continuous Therapy in Two Phase III Trials (BRAVE-AA1 and BRAVE-AA2).

BACKGROUND: The oral Janus kinase (JAK) inhibitor baricitinib has demonstrated efficacy for severe alopecia areata (AA) over 36 weeks. There are limited data on the longer-term treatment of AA. OBJECTIVE: The aim of this study was to evaluate the efficacy and safety of baricitinib for AA in adults with ≥50% scalp hair loss through 52 weeks of continuous therapy in two phase III trials (BRAVE-AA1 and BRAVE-AA2). METHODS: Patients randomized to baricitinib at baseline in BRAVE-AA1 (N = 465) and BRAVE-AA2 (N = 390) retained their treatment allocation through Week 52. Efficacy outcomes included the proportion of patients achieving a Severity of Alopecia Tool (SALT) score ≤ 20 (≤ 20% scalp hair loss). Data were censored after permanent treatment discontinuation or if collected remotely due to the coronavirus disease 2019 (COVID-19) pandemic. RESULTS: Response rates for hair regrowth increased over the 52-week period. Of patients treated with baricitinib 4 mg and 2 mg, respectively, 40.9% and 21.2% in BRAVE-AA1 and 36.8% and 24.4% in BRAVE-AA2 achieved a SALT score ≤ 20 at Week 52. The most frequent treatment-emergent adverse events included upper respiratory tract infection, headache, nasopharyngitis, acne, urinary tract infection, creatine phosphokinase elevation, and COVID-19 infection. LIMITATION: There were no comparisons with placebo. CONCLUSION: Efficacy of baricitinib for adults with severe AA continuously improved over 52 weeks, indicating that long-term treatment may be necessary to observe maximum clinical benefit. There were no new safety signals. CLINICALTRIALS REGISTRATION: ClinicalTrials.gov NCT03570749 and NCT03899259. Efficacy and Safety of Baricitinib in Patients with Severe Alopecia Areata: Week-52 Results from BRAVE-AA1 and BRAVE-AA2.

Alopecia areata (AA) is an autoimmune disease that causes patchy hair loss on the scalp, face, and body. Baricitinib is a Janus kinase inhibitor that is approved to treat AA in several countries, based on results from two studies, BRAVE-AA1 and BRAVE-AA2. In these studies, adults with at least 50% scalp hair loss were treated with baricitinib for 36 weeks. Long-term therapy is important in AA, and hair regrowth can take longer in some patients with severe disease. Therefore, we assessed outcomes from a longer course of therapy. In this study, we report the results after 52 weeks of continuous treatment with baricitinib 4 mg or 2 mg in 465 patients in BRAVE-AA1 and 390 patients BRAVE-AA2. The goal was to reduce scalp hair loss to 20% or less by Week 52. In BRAVE-AA1, 40.9% of patients who took baricitinib 4 mg and 21.2% of patients who took baricitinib 2 mg had 20% or less missing scalp hair by Week 52. Similarly, in BRAVE-AA2, 36.8% of patients who took baricitinib 4 mg and 24.4% of patients who took baricitinib 2 mg had 20% or less missing scalp hair by Week 52. The most common adverse effects that were reported during the study period were upper respiratory tract infection, headache, nasopharyngitis, acne, urinary tract infection, creatine phosphokinase elevation, and coronavirus disease 2019 (COVID-19) infection. The results of longer-term treatment indicate that hair regrowth continues to improve without any new safety concerns for adults with severe AA taking baricitinib.

Matching within a hybrid RCT/RWD: framework on associated causal estimands.

As the regulatory environment becomes progressively receptive toward utilizing real-world evidence, a spectrum of real-world data incorporation techniques in trial conduct and analysis has seen increasing interest and adoption in different stages of drug development. Of particular interest is leveraging external control data to augment the control arm in a concurrent randomized controlled trial, where patients are enrolled in both investigational treatment arm and the control arm. Yet despite the emerging literature in external data borrowing in a hybrid trial setting, very little discussion focuses on delineating what should be matched and what is actually being estimated, especially when a variety of matching schemes can be considered. In general, external control can be matched in four different ways: (1) matching with the intersection between investigational treatment and concurrent control, (2) matching with the union of concurrent investigational treatment and concurrent control, (3) matching with concurrent control alone, and (4) matching with investigational treatment alone. In this article, the formulation of estimands for different matching schemes are detailed to describe what these matching methods facilitate to answer. Simulation studies are also conducted to evaluate the performance characteristics under different matching schemes, estimation methods, effect size assumptions, and missingness of confounders.

Two Phase 3 Trials of Baricitinib for Alopecia Areata.

BACKGROUND: Alopecia areata is an autoimmune condition characterized by rapid hair loss in the scalp, eyebrows, and eyelashes, for which treatments are limited. Baricitinib, an oral, selective, reversible inhibitor of Janus kinases 1 and 2, may interrupt cytokine signaling implicated in the pathogenesis of alopecia areata. METHODS: We conducted two randomized, placebo-controlled, phase 3 trials (BRAVE-AA1 and BRAVE-AA2) involving adults with severe alopecia areata with a Severity of Alopecia Tool (SALT) score of 50 or higher (range, 0 [no scalp hair loss] to 100 [complete scalp hair loss]). Patients were randomly assigned in a 3:2:2 ratio to receive once-daily baricitinib at a dose of 4 mg, baricitinib at a dose of 2 mg, or placebo. The primary outcome was a SALT score of 20 or less at week 36. RESULTS: We enrolled 654 patients in the BRAVE-AA1 trial and 546 in the BRAVE-AA2 trial. The estimated percentage of patients with a SALT score of 20 or less at week 36 was 38.8% with 4-mg baricitinib, 22.8% with 2-mg baricitinib, and 6.2% with placebo in BRAVE-AA1 and 35.9%, 19.4%, and 3.3%, respectively, in BRAVE-AA2. In BRAVE-AA1, the difference between 4-mg baricitinib and placebo was 32.6 percentage points (95% confidence interval [CI], 25.6 to 39.5), and the difference between 2-mg baricitinib and placebo was 16.6 percentage points (95% CI, 9.5 to 23.8) (P<0.001 for each dose vs. placebo). In BRAVE-AA2, the corresponding values were 32.6 percentage points (95% CI, 25.6 to 39.6) and 16.1 percentage points (95% CI, 9.1 to 23.2) (P<0.001 for each dose vs. placebo). Secondary outcomes for baricitinib at a dose of 4 mg but not at a dose of 2 mg generally favored baricitinib over placebo. Acne, elevated levels of creatine kinase, and increased levels of low- and high-density lipoprotein cholesterol were more common with baricitinib than with placebo. CONCLUSIONS: In two phase 3 trials involving patients with severe alopecia areata, oral baricitinib was superior to placebo with respect to hair regrowth at 36 weeks. Longer trials are required to assess the efficacy and safety of baricitinib for alopecia areata. (Funded by Eli Lilly under license from Incyte; BRAVE-AA1 and BRAVE-AA2 ClinicalTrials.gov numbers, NCT03570749 and NCT03899259.).

Power priors with entropy balancing weights in data augmentation of partially controlled randomized trials.

In pediatric or orphan diseases, there are many instances where it is unfeasible to conduct randomized and controlled clinical trials. This is due in part to the difficulty of enrolling a sufficient number of patients over a reasonable time period to meet adequate statistical power to demonstrate the treatment efficacy. One solution to reduce the sample size or expedite the trial timeline is to complement the current trial with real-world data. To this end, several propensity score-based methods have been developed to create defined groups of patients that are controlled for confounding based on a set of measured covariates at baseline. However, balance checking on the measured covariates and tweaks to the propensity score models is usually inevitable to achieve the joint balance across all covariates. To mitigate this iterative procedure, we utilize the entropy balancing weighting technique which focuses on balancing the covariates of subjects between the experimental and control groups directly and augments the current trial with the external control data via a power prior. The finite-sample properties of the proposed method are assessed via simulations in the context of asymmetrically randomized controlled trials where only a small portion of patients are randomized to the control group. Other methods such as covariate-balancing propensity score (CBPS) and propensity score matching (PSM) and weighting (PSW) are also compared to provide context on the operating characteristics of the proposed method.

Prediction and risk stratification from hospital discharge records based on Hierarchical sLDA.

BACKGROUND: The greatly accelerated development of information technology has conveniently provided adoption for risk stratification, which means more beneficial for both patients and clinicians. Risk stratification offers accurate individualized prevention and therapeutic decision making etc. Hospital discharge records (HDRs) routinely include accurate conclusions of diagnoses of the patients. For this reason, in this paper, we propose an improved model for risk stratification in a supervised fashion by exploring HDRs about coronary heart disease (CHD). METHODS: We introduced an improved four-layer supervised latent Dirichlet allocation (sLDA) approach called Hierarchical sLDA model, which categorized patient features in HDRs as patient feature-value pairs in one-hot way according to clinical guidelines for lab test of CHD. To address the data missing and imbalance problem, RFs and SMOTE methods are used respectively. After TF-IDF processing of datasets, variational Bayes expectation-maximization method and generalized linear model were used to recognize the latent clinical state of a patient, i.e., risk stratification, as well as to predict CHD. Accuracy, macro-F1, training and testing time performance were used to evaluate the performance of our model. RESULTS: According to the characteristics of our datasets, i.e., patient feature-value pairs, we construct a supervised topic model by adding one more Dirichlet distribution hyperparameter to sLDA. Compared with established supervised algorithm Multi-class sLDA model, we demonstrate that our proposed approach enhances training time by 59.74% and testing time by 25.58% but almost no loss of average prediction accuracy on our datasets. CONCLUSIONS: A model for risk stratification and prediction of CHD based on sLDA model was proposed. Experimental results show that Hierarchical sLDA model we proposed is competitive in time performance and accuracy. Hierarchical processing of patient features can significantly improve the disadvantages of low efficiency and time-consuming Gibbs sampling of sLDA model.

Efficacy and safety of the oral Janus kinase inhibitor baricitinib in the treatment of adults with alopecia areata: Phase 2 results from a randomized controlled study.

BACKGROUND: There are no treatments approved by the Food and Drug Administration for alopecia areata. OBJECTIVE: To evaluate the efficacy and safety of baricitinib in patients with ≥50% scalp hair loss in a phase 2 study of adults with alopecia areata (BRAVE-AA1). METHODS: Patients were randomized 1:1:1:1 to receive placebo or baricitinib 1 mg, 2 mg, or 4 mg once daily. Two consecutive interim analyses were performed after all patients completed weeks 12 and 36 or had discontinued treatment prior to these time points. The primary endpoint was the proportion of patients achieving a Severity of Alopecia Tool (SALT) score ≤20 at week 36. Logistic regression was used with nonresponder imputation for missing data. RESULTS: A total of 110 patients were randomized (placebo, 28; baricitinib 1-mg, 28; 2-mg, 27; 4-mg, 27). The baricitinib 1-mg dose was dropped after the first interim analysis based on lower SALT30 response rate. At week 36, the proportion of patients achieving a SALT score of ≤20 was significantly greater in baricitinib 2-mg (33.3%, P = .016) and 4-mg (51.9%, P = .001) groups versus placebo (3.6%). Baricitinib was well tolerated with no new safety findings. LIMITATIONS: Small sample size limits generalizability of results. CONCLUSION: These results support the efficacy and safety of baricitinib in patients with ≥50% scalp hair loss.

The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring.

The win ratio method has received much attention in methodological research, ad hoc analyses, and designs of prospective studies. As the primary analysis, it supported the approval of tafamidis for the treatment of cardiomyopathy to reduce cardiovascular mortality and cardiovascular-related hospitalization. However, its dependence on censoring is a potential shortcoming. In this article, we propose the inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic (i.e., the IPCW-adjusted win ratio statistic) to overcome censoring issues. We consider independent censoring, common censoring across endpoints, and right censoring. We develop an asymptotic variance estimator for the logarithm of the IPCW-adjusted win ratio statistic and evaluate it via simulation. Our simulation studies show that, as the amount of censoring increases, the unadjusted win proportions may decrease greatly. Consequently, the bias of the unadjusted win ratio estimate may increase greatly, producing either an overestimate or an underestimate. We demonstrate theoretically and through simulation that the IPCW-adjusted win ratio statistic gives an unbiased estimate of treatment effect.

An Additive-Multiplicative Mean Model for Panel Count Data with Dependent Observation and Dropout Processes.

This paper discusses regression analysis of panel count data with dependent observation and dropout processes. For the problem, a general mean model is presented that can allow both additive and multiplicative effects of covariates on the underlying point process. In addition, the proportional rates model and the accelerated failure time model are employed to describe possible covariate effects on the observation process and the dropout or follow-up process, respectively. For estimation of regression parameters, some estimating equation-based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for estimating covariance matrix of the proposed estimator and a model checking procedure is also provided. Results from an extensive simulation study indicate that the proposed methodology works well for practical situations and it is applied to a motivating set of real data.

Regression analysis of incomplete data from event history studies with the proportional rates model.

This paper discusses regression analysis of a type of incomplete mixed data arising from event history studies with the proportional rates model. By mixed data, we mean that each study subject may be observed continuously during the whole study period, continuously over some study periods and at some time points, or only at some discrete time points. Therefore, we have combined recurrent event and panel count data. For the problem, we present a multiple imputation-based estimation procedure and one advantage of the proposed marginal model approach is that it can be easily implemented. To assess the performance of the procedure, a simulation study is conducted and indicates that it performs well for practical situations and can be more efficient than the existing method. The methodology is applied to a set of mixed data from a longitudinal cohort study.

Regression analysis of mixed panel count data with dependent terminal events.

Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd.

Mechanosensing of cells in 3D gel matrices based on natural and synthetic materials.

Cells in vivo typically are found in 3D matrices, the mechanical stiffness of which is important to the cell and tissue-scale biological processes. Although it is well characterized that as to how cells sense matrix stiffness in 2D substrates, the scenario in 3D matrices needs to be explored. Thus, materials that can mimic native 3D environments and possess wide, physiologically relevant elasticity are highly desirable. Natural polymer-based materials and synthetic hydrogels could provide an better 3D platforms to investigate the mechano-response of cells with stiffness comparable to their native environments. However, the limited stiffness range together with interdependence of matrix stiffness and adhesive ligand density are inherent in many kinds of materials, and hinder efforts to demonstrate the true effects contributed by matrix stiffness. These problems have been addressed by the recently emerging exquisitely designed materials based on native matrix components, designer matrices, and synthetic polymers. In this review, a variety of materials with a wide stiffness range that mimic the mechanical environment of native 3D matrices and the independent affection of stiffness for cellular behavior and tissue-level processes are discussed.