Challenges in Evaluating Safety and Efficacy in Drug Development for Rare Diseases: A Review for Pharmacists

Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases

The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.

Real world data (RWD) in pediatrics

Unique challenges pertain when studying children, although many research principles are the same as those when studying adult populations. This truism extends to the use of real-world data (RWD). RWD are particularly relevant to pediatrics because they may potentially provide an additional source of data to inform pediatric labeling and practice patterns when clinical trials have not been or cannot be conducted. The purpose of this commentary is to provide a brief overview of the unique issues in using RWD to study the effectiveness or safety of medical therapies in children.

A rhein-huprine hybrid protects erythrocyte membrane integrity against Alzheimer's disease related Aβ(1-42) peptide

Alzheimer's disease remains largely unknown, and currently there is no complete cure for the disease. New synthetic approaches have been developed to create multi-target agents, such as RHE-HUP, a rhein-huprine hybrid which can modulate several biological targets that are relevant to the development of the disease. While RHE-HUP has shown in vitro and in vivo beneficial effects, the molecular mechanisms by which it exerts its protective effect on cell membranes have not been fully clarified. To better understand RHE-HUP interactions with cell membranes, we used synthetic membrane models and natural models of human membranes. For this purpose, human erythrocytes and molecular model of its membrane built-up of dimyristoylphosphatidylcholine (DMPC) and dimyristoylphosphatidylethanolamine (DMPE) were used. The latter correspond to classes of phospholipids present in the outer and inner monolayers of the human erythrocyte membrane, respectively. X-ray diffraction and differential scanning calorimetry (DSC) results indicated that RHE-HUP was able to interact mainly with DMPC. In addition, scanning electron microscopy (SEM) analysis showed that RHE-HUP modified the normal biconcave shape of erythrocytes inducing the formation of echinocytes. Moreover, the protective effect of RHE-HUP against the disruptive effect of Aβ(1-42) on the studied membrane models was tested. X-ray diffraction experiments showed that RHE-HUP induced a recovery in the ordering of DMPC multilayers after the disruptive effect of Aβ(1-42), confirming the protective role of the hybrid.

Pharmacists' Knowledge and Perceptions of FDA Approval Standards and the Breakthrough Therapy Designation

The “breakthrough therapy” designation (BTD) is a recent mechanism implemented by the United States Food and Drug Administration (FDA) to expedite access to drugs that address unmet needs. The purpose of this study is to describe pharmacists’ knowledge of FDA drug-approval standards and knowledge and perceptions of the BTD. Pharmacists engaged in advanced clinical practice were identified through membership profiles of a professional pharmacy organization. Eligible participants were then sent a questionnaire to assess knowledge of FDA approval standards and the BTD. A total of 226 pharmacists responded. The majority of respondents were women (70.2%) and had completed post-graduate training (85.8%). Over half correctly answered at least two of three questions on FDA approval standards (58.1%) and the BTD (78.1%). Only 24.1% of respondents identified as being familiar with the BTD. The majority of pharmacists (62.8%) were certain that FDA-approved “breakthrough” drugs represented a major advance over currently approved therapies and most (88.5%) preferred the drug designated as “breakthrough” in a hypothetical scenario. In conclusion, pharmacists were able to correctly answer questions about FDA approval standards and the BTD. However, they were unfamiliar with the implications of a BTD and may overestimate the benefit demonstrated by these drugs. Future research should identify knowledge gaps in pharmacist understanding of regulatory mechanisms designed to expedite drug approval.

Use of funded multicenter prospective longitudinal databases to inform clinical trials in rare diseases-Examination of cholestatic liver disease in Alagille syndrome

The conduct of long-term conventional randomized clinical trials in rare diseases is very difficult, making evidenced-based drug development problematic. As a result, real-world data/evidence are being used more frequently to assess new therapeutic approaches in orphan diseases. In this investigation, inclusion and exclusion criteria from a published trial of maralixibat in Alagille syndrome (ALGS, ITCH NCT02057692) were applied to a prospective longitudinal cohort of children with cholestasis (LOGIC NCT00571272) to derive contextual comparator data for evolving clinical trials of intestinal bile acid transport inhibitors in ALGS. A natural history/clinical care cohort of 59 participants who met adapted inclusion and exclusion criteria of ITCH was identified from 252 LOGIC participants with ALGS with their native liver. Frequency weighting was used to match the age distribution of ITCH and yielded a cohort (Alagille Syndrome Natural History [ALGS NH]) that was very similar to the baseline status of ITCH participants. During a 2-year prospective follow-up there was a significant reduction in pruritus in the weighted ALGS NH cohort as assessed by the clinician scratch score (-1.43 [0.28] -1.99, -0.87; mean [SEM] 95% confidence interval). During the same time period, the total bilirubin, albumin, and alanine aminotransferase levels were unchanged, whereas platelet count dropped significantly (-65.2 [16.2] -98.3, -32.1). Weighted survival with native liver was 91% at 2 years in the ALGS NH. These investigations provide valuable real-world data that can serve as contextual comparators to current clinical trials, especially those without control populations, and highlight the value and importance of funded multicenter, prospective, natural history studies.

AI-based protein structure databases have the potential to accelerate rare diseases research: AlphaFoldDB and the case of IAHSP/Alsin

Artificial intelligence (AI)-based protein structure databases are expected to have an impact on drug discovery. Here, we show how AlphaFold could support rare diseases research programs. We focus on Alsin, a protein responsible for rare motor neuron diseases, such as infantile-onset ascending hereditary spastic paralysis (IAHSP) and juvenile primary lateral sclerosis (JPLS), and involved in some cases of amyotrophic lateral sclerosis (ALS). First, we compared the AlphaFoldDB human Alsin model with homology models of alsin domains. We then evaluated the flexibility profile of Alsin and of experimentally characterized mutants present in patients with IAHSP. Next, we compared preliminary models of dimeric/tetrameric Alsin responsible for its physiological action with hypothetical models reported in the literature. Finally, we suggest the best animal model for drug candidates testing. Overall, we computationally show that drug discovery efforts toward Alsin-involving diseases should be pursued.