Recent successes and future predictions on drug repurposing for rare diseases

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Drug Repurposing: A Leading Strategy for New Threats and Targets

Less than 6% of rare illnesses have an appropriate treatment option. Repurposed medications for new indications are a cost-effective and time-saving strategy that results in excellent success rates, which may significantly lower the risk associated with therapeutic development for rare illnesses. It is becoming a realistic alternative to repurposing "conventional" medications to treat joint and rare diseases considering the significant failure rates, high expenses, and sluggish stride of innovative medication advancement. This is due to delisted compounds, cheaper research fees, and faster development time frames. Repurposed drug competitors have been developed using strategic decisions based on data analysis, interpretation, and investigational approaches, but technical and regulatory restrictions must also be considered. Combining experimental and computational methodologies generates innovative new medicinal applications. It is a one-of-a-kind strategy for repurposing human-safe pharmaceuticals to treat uncommon and difficult-to-treat ailments. It is a very effective method for discovering and creating novel medications. Several pharmaceutical firms have developed novel therapies by repositioning old medications. Repurposing drugs is practical, cost-effective, and speedy and generally involves lower risks when compared to developing a new drug from the beginning.

How can natural language processing help model informed drug development?: a review

Objective

To summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement.

Materials and Methods

Publications found on PubMed and Google Scholar, websites and GitHub repositories for NLP libraries and models. Publications describing applications of NLP in MIDD were reviewed. The applications were stratified into 3 stages: drug discovery, clinical trials, and pharmacovigilance. Key NLP functionalities used for these applications were assessed. Programming libraries and open-source resources for the implementation of NLP functionalities in MIDD were identified.

Results

NLP has been utilized to aid various processes in drug development lifecycle such as gene-disease mapping, biomarker discovery, patient-trial matching, adverse drug events detection, etc. These applications commonly use NLP functionalities of named entity recognition, word embeddings, entity resolution, assertion status detection, relation extraction, and topic modeling. The current state-of-the-art for implementing these functionalities in MIDD applications are transformer models that utilize transfer learning for enhanced performance. Various libraries in python, R, and Java like huggingface, sparkNLP, and KoRpus as well as open-source platforms such as DisGeNet, DeepEnroll, and Transmol have enabled convenient implementation of NLP models to MIDD applications.

Discussion

Challenges such as reproducibility, explainability, fairness, limited data, limited language-support, and security need to be overcome to ensure wider adoption of NLP in MIDD landscape. There are opportunities to improve the performance of existing models and expand the use of NLP in newer areas of MIDD.

Conclusions

This review provides an overview of the potential and pitfalls of current NLP approaches in MIDD.

Adverse Events Related to Off-Label Drugs Using Spontaneous Adverse Event Reporting Systems

Purpose

The purpose of this study was to investigate the adverse events (AEs) related to the use of off-label drugs.

Materials and Methods

A cross-sectional study was carried out using available data pertaining to off-label drug were sourced from U.S. FDA spontaneous adverse drug reaction reporting database (FAERS) and Korea Adverse Event Reporting System database (KIDS-KD) for the years 2014 to 2018. The number and frequencies of AE cases were calculated. Disproportionality was analyzed using the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM) methods.

Results

The reported AEs associated with off-label drug use were more common among older patients compared with younger patients. Gastric nonspecific symptoms and therapeutic procedure (4.16–4.57%) and haemorrage term (4.16–5.29%) were the most common AE symptoms and antithrombotic agents and immunosuppressants were the drugs most commonly reported to cause AEs in FAERS. Secondary term events (43.45–48.62%) including inappropriate schedule of drug administration and medication error were the most common AEs, and immunosuppressants and antipsychotics were the most common AE-related drugs from KIDS-KD. The numbers of reported AEs in new drug categories such as other antineoplastic agents trended to increase from 2014 to 2018 in both datasets.

Conclusion

The numbers of reported AEs with off-label drug increased annually. AEs associated with off-label drugs may have a significant impact on older patients. Healthcare experts should be concerned about prescriptions of off-label drugs, especially anticoagulants and newly developed drugs such as immunosuppressants and antineoplastic agents.

Exploring new uses for existing drugs: innovative mechanisms to fund independent clinical research

BACKGROUND

Finding new therapeutic uses for existing medicines could lead to safe, affordable and timely new treatment options for patients with high medical needs. However, due to a lack of economic incentives, pharmaceutical developers are rarely interested to invest in research with approved medicines, especially when they are out of basic patent or regulatory protection. Consequently, potential new uses for these medicines are mainly studied in independent clinical trials initiated and led by researchers from academia, research institutes, or collaborative groups. Yet, additional financial support is needed to conduct expensive phase III clinical trials to confirm the results from exploratory research.

METHODS

In this study, scientific and grey literature was searched to identify and evaluate new mechanisms for funding clinical trials with repurposed medicines. Semi-structured interviews were conducted with 16 European stakeholders with expertise in clinical research, funding mechanisms and/or drug repurposing between November 2018 and February 2019 to consider the future perspectives of applying new funding mechanisms.

RESULTS

Traditional grant funding awarded by government and philanthropic organisations or companies is well known and widely implemented in all research fields. In contrast, only little research has focused on the application potential of newer mechanisms to fund independent clinical research, such as social impact bonds, crowdfunding or public-private partnerships. Interviewees stated that there is a substantial need for additional financial support in health research, especially in areas where there is limited commercial interest. However, the implementation of new funding mechanisms is facing several practical and financial challenges, such as a lack of expertise and guidelines, high transaction costs and difficulties to measure health outcomes. Furthermore, interviewees highlighted the need for increased collaboration and centralisation at a European and international level to make clinical research more efficient and reduce the need for additional funding.

CONCLUSIONS

New funding mechanisms to support clinical research may become more important in the future but the unresolved issues identified in the current study warrant further exploration.

Harnessing Drug Repurposing for Exploration of New Diseases: An Insight to Strategies and Case Studies

BACKGROUND

Traditional drug discovery is time consuming, costly, and risky process. Owing to the large investment, excessive attrition, and declined output, drug repurposing has become a blooming approach for the identification and development of new therapeutics. The method has gained momentum in the past few years and has resulted in many excellent discoveries. Industries are resurrecting the failed and shelved drugs to save time and cost. The process accounts for approximately 30% of the new US Food and Drug Administration approved drugs and vaccines in recent years.

METHODS

A systematic literature search using appropriate keywords were made to identify articles discussing the different strategies being adopted for repurposing and various drugs that have been/are being repurposed.

RESULTS

This review aims to describe the comprehensive data about the various strategies (Blinded search, computational approaches, and experimental approaches) used for the repurposing along with success case studies (treatment for orphan diseases, neglected tropical disease, neurodegenerative diseases, and drugs for pediatric population). It also inculcates an elaborated list of more than 100 drugs that have been repositioned, approaches adopted, and their present clinical status. We have also attempted to incorporate the different databases used for computational repurposing.

CONCLUSION

The data presented is proof that drug repurposing is a prolific approach circumventing the issues poised by conventional drug discovery approaches. It is a highly promising approach and when combined with sophisticated computational tools, it also carries high precision. The review would help researches in prioritizing the drugrepositioning method much needed to flourish the drug discovery research.

A computational drug repositioning method applied to rare diseases: Adrenocortical carcinoma

Rare or orphan diseases affect only small populations, thereby limiting the economic incentive for the drug development process, often resulting in a lack of progress towards treatment. Drug repositioning is a promising approach in these cases, due to its low cost. In this approach, one attempts to identify new purposes for existing drugs that have already been developed and approved for use. By applying the process of drug repositioning to identify novel treatments for rare diseases, we can overcome the lack of economic incentives and make concrete progress towards new therapies. Adrenocortical Carcinoma (ACC) is a rare disease with no practical and definitive therapeutic approach. We apply Heter-LP, a new method of drug repositioning, to suggest novel therapeutic avenues for ACC. Our analysis identifies innovative putative drug-disease, drug-target, and disease-target relationships for ACC, which include Cosyntropin (drug) and DHCR7, IGF1R, MC1R, MAP3K3, TOP2A (protein targets). When results are analyzed using all available information, a number of novel predicted associations related to ACC appear to be valid according to current knowledge. We expect the predicted relations will be useful for drug repositioning in ACC since the resulting ranked lists of drugs and protein targets can be used to expedite the necessary clinical processes.

Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications

We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

Drug repurposing: a promising tool to accelerate the drug discovery process

Traditional drug discovery and development involves several stages for the discovery of a new drug and to obtain marketing approval. It is necessary to discover new strategies for reducing the drug discovery time frame. Today, drug repurposing has gained importance in identifying new therapeutic uses for already-available drugs. Typically, repurposing can be achieved serendipitously (unintentional fortunate observations) or through systematic approaches. Numerous strategies to discover new indications for FDA-approved drugs are discussed in this article. Drug repurposing has therefore become a productive approach for drug discovery because it provides a novel way to explore old drugs for new use but encounters several challenges. Some examples of different approaches are reviewed here.

Dexpramipexole as an oral steroid-sparing agent in hypereosinophilic syndromes

Hypereosinophilic syndromes (HESs) are a heterogeneous group of disorders characterized by peripheral eosinophilia and eosinophil-related end organ damage. Whereas most patients respond to glucocorticoid (GC) therapy, high doses are often necessary, and side effects are common. Dexpramipexole (KNS-760704), an orally bioavailable synthetic aminobenzothiazole, showed an excellent safety profile and was coincidentally noted to significantly decrease absolute eosinophil counts (AECs) in a phase 3 trial for amyotrophic lateral sclerosis. This proof-of-concept study was designed to evaluate dexpramipexole (150 mg orally twice daily) as a GC-sparing agent in HESs. Dual primary end points were (1) the proportion of subjects with ≥50% decrease in the minimum effective GC dose (MED) to maintain AEC <1000/μL and control clinical symptoms, and (2) the MED after 12 weeks of dexpramipexole (MEDD) as a percentage of the MED at week 0. Out of 10 subjects, 40% (95% confidence interval [CI], 12%, 74%) achieved a ≥50% reduction in MED, and the MEDD/MED ratio was significantly <100% (median, 66%; 95% CI, 6%, 98%; P = .03). All adverse events were self-limited, and none led to drug discontinuation. Affected tissue biopsy samples in 2 subjects showed normalization of pathology and depletion of eosinophils on dexpramipexole. Bone marrow biopsy samples after 12 weeks of dexpramipexole showed selective absence of mature eosinophils in responders. Dexpramipexole appears promising as a GC-sparing agent without apparent toxicity in a subset of subjects with GC-responsive HESs. Although the exact mechanism of action is unknown, preliminary data suggest that dexpramipexole may affect eosinophil maturation in the bone marrow. This study was registered at www.clinicaltrials.gov as #NCT02101138.

Design, Synthesis and Biological Evaluation of 2-(2-Amino-5(6)-nitro-1H-benzimidazol-1-yl)-N-arylacetamides as Antiprotozoal Agents

Parasitic diseases are a public health problem affecting millions of people worldwide. One of the scaffolds used in several drugs for the treatment of parasitic diseases is the benzimidazole moiety, a heterocyclic aromatic compound. This compound is a crucial pharmacophore group and is considered a privileged structure in medicinal chemistry. In this study, the benzimidazole core served as a model for the synthesis of a series of 2-(2-amino-5(6)-nitro-1H-benzimidazol-1-yl)-N-arylacetamides 1–8 as benznidazole analogues. The in silico pharmacological results calculated with PASS platform exhibited chemical structures highly similar to known antiprotozoal drugs. Compounds 1–8 when evaluated in silico for acute toxicity by oral dosing, were less toxic than benznidazole. The synthesis of compounds 1–8 were carried out through reaction of 5(6)-nitro-1H-benzimidazol-2-amine (12) with 2-chlroactemides 10a–h, in the presence of K₂CO₃ and acetonitrile as solvent, showing an inseparable mixture of two regioisomers with the -NO₂ group in position 5 or 6 with chemical yields of 60 to 94%. The prediction of the NMR spectra of molecule 1 coincided with the experimental chemical displacements of the regioisomers. Comparisons between the NMR prediction and the experimental data revealed that the regioisomer endo-1,6-NO₂ predominated in the reaction. The in vitro antiparasitic activity of these compounds on intestinal unicellular parasites (Giardia intestinalis and Entamoeba histolytica) and a urogenital tract parasite (Trichomonas vaginalis) were tested. Compound 7 showed an IC₅₀ of 3.95 μM and was 7 time more active against G. intestinalis than benznidazole. Compounds 7 and 8 showed 4 times more activity against T. vaginalis compared with benznidazole.