Opportunities and Challenges for Drug Development: Public-Private Partnerships, Adaptive Designs and Big Data

Approved drugs successfully repurposed against Leishmania based on machine learning predictions

Drug repurposing is a promising approach towards the discovery of novel treatments against Neglected Tropical Diseases, such as Leishmaniases, presenting the advantage of reducing both costs and duration of the drug discovery process. In previous work, our group developed a Machine Learning pipeline for the repurposing of FDA-approved drugs against Leishmania parasites. The present study is focused on an in vitro validation of this approach by assessing the antileishmanial effects of 10 predicted drug candidates. First, we evaluated the drugs' activity against promastigotes from two strains of L. infantum and one of L. major, which caused distinct clinical manifestations, using an MTT assay. The standard anti-Leishmania drug Amphotericin B was used as a positive control. Five molecules demonstrated anti-Leishmania effects, out of which Acebutolol, Prilocaine and Phenylephrine are described herein for the first time. When tested on promastigote growth, Acebutolol displayed IC50 values ranging from 69.28 to 145.53 µg/mL. Prilocaine exhibited IC50 values between 33.10 and 45.81 µg/mL. Phenylephrine, on the other hand, presented IC50 values >200 µg/mL. The two remaining drugs, Dibucaine and Domperidone, exhibited significantly low IC50 values varying between 0.58 and 1.05 µg/mL, and 6.30 and 8.17 µg/mL, respectively. Both compounds were previously described as anti-Leishmania agents in vivo. All five compounds demonstrated no notable cytotoxic effects on THP-1-derived macrophages at the IC50 concentrations, allowing for their testing on the intracellular form of L. major and L. infantum parasites. Interestingly, all compounds exhibited antileishmanial activity on amastigotes with enhanced IC50 values compared to the corresponding promastigotes. Noticeably, Dibucaine and Domperidone displayed IC50 values of at most 1.99 µg/mL. Acebutolol, Prilocaine and Phenylephrine showed IC50 values ranging from 13.84 to 66.81 µg/mL. Our previously published Computer-Aided repositioning pipelines of FDA-approved drugs as antileishmanial agents identified Dibucaine and Domperidone as candidates in support of previous in vivo studies. This study consolidates such findings through the in vitro validation against 2 Leishmania species, highly prevalent in Africa and Middle East, and reveals Acebutolol, Prilocaine, and Phenylephrine as novel anti-Leishmania effectors, confirming the relevance of our approach and calling for further investigations.

Developing public-private R&D consortia to accelerate Alzheimer's disease drug development

Efforts to accelerate Alzheimer's disease (AD) drug development have been spurred on by the creation of open science, public-private R&D consortia. An R&D consortium provides an improved structure for generating and disseminating AD knowledge across a range of organizations while also aligning their interests. Drawing from archival and interview data collected on 46 public-private R&D consortia focused wholly or in part on AD, we uncover two important innovations: the creation of novel consortium types that facilitate coordination beyond the individual consortium, and the practice of organizations joining multiple consortia. Collectively these innovations provide member organizations with different pathways for advancing AD research. These findings have significant implications for how member organizations should approach collaboration in the AD drug development process.

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

The Alliance Between the German Cancer Research Center and Bayer: A Retrospective of an Innovative Collaboration Model

In 2009, the German Cancer Research Center (DKFZ) and the biopharmaceutical company Bayer AG initiated an academic-industry co-discovery collaboration. The partners combined their expertise in tumor biology and drug discovery to identify and validate novel targets for cancer treatment. In the early phase of the Alliance, the focus was on target identification and validation projects. Over time, both partners realized that they could also successfully collaborate on later stages of drug discovery. As a result over the past few years, and following several contract extensions, the two partners have collaborated on several late-stage drug discovery projects. This has resulted in the achievement of several drug discovery milestones and the initiation of early clinical trials, the most recent in 2022. This success has been possible thanks to both partners' understanding of each other's needs and challenges. They jointly developed solutions to issues such as the intrinsic potential conflict of early publishing versus patent protection. Both partners also appreciated the risks involved in some of the experiments, such as starting a joint laboratory for immune-therapy with scientists from both parties working bench-to-bench. Recently, despite these successes the partners decided to terminate the Alliance, as Bayer AG wants to focus its activities on the development of its late pipeline.

Copper-catalyzed aerobic annulation of hydrazones with dienones: an efficient route to pyrazole-linked hybrid molecules

A copper-catalyzed aerobic [3 + 2] annulation reaction to access various pyrazole-bound chalcones starting from readily available and cost-effective hydrazones and dienones is reported. These pyrazole-bound chalcones were further utilized effectively to prepare a series of pyrazole-linked hybrid molecules, such as pyrazole-pyrazoline, pyrazole-aziridine, and pyrazole-pyridine hybrids by efficient simple transformations. Synthetically challenging hybrid molecules were obtained in a simple, two-step process with high atom economy under aerobic copper catalysis.

Project-Based Public-Private Collaborations

Project-based collaborations between a single academic group and a single pharmaceutical company arguably are the most frequent form of public-private partnership in preclinical research and development of new drugs. This chapter discusses the benefits of such collaborations for both sides and potential challenges that can arise before and during the conduct of a project. This is largely based on a survey of expectations and experience by 134 academic investigators with a history of engagement in a project-based collaboration with a pharmaceutical company as well as unstructured experience directly, and learned through discussions with colleagues, from the authors. Obviously, a key benefit for both sides is achieving goals that neither could easily achieve by itself. Scientific discovery, and publications, may be a shared benefit, while for academics, funding and access to compounds, and for industry, access to assay technology and reputational factors may be important. Major hurdles can be freedom to publish and assignment of intellectual property rights. On pragmatic grounds, reaching a contract can be cumbersome, which is largely attributable to the legal expectations and needs of both parties. However, overall satisfaction with project-based collaborations appears very high for academic investigators.

Analysis of research topics and trends in investigator-initiated research/trials (IIRs/IITs): A topic modeling study

BACKGROUND

With the exponential growth of publications in the field of investigator-initiated research/trials (IIRs/IITs), it has become necessary to employ text mining and bibliometric analysis as tools for gaining deeper insights into this area of study. By using these methods, researchers can effectively identify and analyze research topics within the field.

METHODS

This study retrieved relevant publications from the Web of Science Core Collection and conducted bioinformatics analysis. The latent Dirichlet allocation model, which is based on machine learning, was utilized to identify subfield research topics.

RESULTS

A total of 4315 articles related to IIRs/IITs were obtained from the Web of Science Core Collection. After excluding duplicates and articles with missing abstracts, a final dataset of 3333 articles was included for bibliometric analysis. The number of publications showed a steady increase over time, particularly since 2000. The United States, Germany, the United Kingdom, the Netherlands, Canada, Denmark, Japan, Switzerland, and France emerged as the most productive countries in terms of IIRs/IITs. The citation analysis revealed intriguing trends, with certain highly cited articles showing a significant increase in citation frequency in recent years. A model with 45 topics was deemed the best fit for characterizing the extensively researched fields within IIRs/IITs. Our analysis revealed 10 top topics that have garnered significant attention, spanning domains such as community health, cancer treatment, brain development and disease mechanisms, nursing research, and stem cell therapy. These top topics offer researchers valuable directions for further investigation and innovation. Additionally, we identified 12 hot topics, which represent the most cutting-edge and highly regarded research areas within the field.

CONCLUSION

This study contributes to a comprehensive understanding of the current research landscape and provides valuable insights for researchers working in this domain.

Repurposing of FDA approved kinase inhibitor bosutinib for mitigation of radiation induced damage via inhibition of JNK pathway

Radiotherapy is a common modality for cancer treatment. However, it is often associated with normal tissue toxicity in 20-80% of the patients. Radioprotectors can improve the outcome of radiotherapy by selectively protecting normal cells against radiation toxicity. In the present study, compound libraries containing 54 kinase inhibitors and 80 FDA-approved drugs were screened for radioprotection of lymphocytes using high throughput cell analysis. A second-generation FDA-approved kinase inhibitor, bosutinib, was identified as a potential radioprotector for normal cells. The radioprotective efficacy of bosutinib was evinced from a reduction in radiation induced DNA damage, caspase-3 activation, DNA fragmentation and apoptosis. Oral administration of bosutinib protected mice against whole body irradiation (WBI) induced morbidity and mortality. Bosutinib also reduced radiation induced bone-marrow aplasia and hematopoietic damage in mice exposed to 4 Gy and 6 Gy dose of WBI. Mechanistic studies revealed that the radioprotective action of bosutinib involved interaction with cellular thiols and modulation of JNK pathway. The addition of glutathione and N-acetyl cysteine significantly reduced the radioprotective efficacy of bosutinib. Moreover, bosutinib did not protect cancer cells against radiation induced toxicity. On the contrary, bosutinib per se exhibited anticancer activity against human cancer cell lines. The results highlight possible use of bosutinib as a repurposable radioprotective agent for mitigation of radiation toxicity in cancer patients undergoing radiotherapy.

Lugdunin production and activity in Staphylococcus lugdunensis isolates are associated with its genotypes

Lugdunin produced by Staphylococcus lugdunensis has been shown to have broad inhibitory activity against Gram-positive bacteria; however, lugdunin activity among S. lugdunensis isolates and its association with different agr, SCCmec, and sequence types remain unclear. We used matrix-assisted laser desorption ionization-time-of-flight mass spectrometry to identify S. lugdunensis and collected 202 S. lugdunensis samples for further assays. Agar spot tests were performed to characterize S. lugdunensis lugdunin production and activity. Multilocus sequence typing, SCCmec, and agr genotyping were performed on S. lugdunensis. In all, 91 Staphylococcus aureus strains with varying vancomycin susceptibilities were used to examine lugdunin activity in S. lugdunensis. In total, 48 S. lugdunensis strains (23.8%) were found to be oxacillin-resistant S. lugdunensis (ORSL), whereas 154 (76.2%) were classified as oxacillin-sensitive S. lugdunensis (OSSL). Moreover, 16 (33.3%) ORSL and 35 (22.7%) OSSL strains showed antibacterial activity against S. aureus. Our data showed that most lugdunin-producing ORSL strains (14/48, 29.2%) were of ST3-SCCmec V-agr II genotypes, whereas most lugdunin-producing OSSL strains (15/154, 9.7%) were of ST3-agr II, followed by ST1-agr I (10/154, 6.5%). Our data also revealed that lugdunin exhibited weak inhibitory activity against the VISA ST239 isolate. In addition, we observed that ST239 VSSA was more resistant to lugdunin than ST5, ST59, and ST45 VSSA. Taken together, our data pioneered the epidemiology of lugdunin production in S. lugdunensis isolates and revealed its association with genotypes. However, further molecular and bioinformatics investigations are needed to elucidate the regulatory mechanisms of lugdunin production and activity. IMPORTANCE Lugdunin is active against both methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci by dissipating their membrane potential. However, the association of lugdunin activity with the genotypes of Staphylococcus lugdunensis has not been addressed. Here, we show the high prevalence of lugdunin-producing strains among ST1 (83.3%), ST2 (66.7%), and ST3 (53.3%) S. lugdunensis. Moreover, we identified the antibacterial activity of lugdunin-producing strains against VISA and hVISA. These results shed light on the potential application of lugdunin for the treatment of drug-resistant pathogens.

Aminochalcones Attenuate Neuronal Cell Death under Oxidative Damage via Sirtuin 1 Activity

Encouraged by the lack of effective treatments and the dramatic growth in the global prevalence of neurodegenerative diseases along with various pharmacological properties of chalcone pharmacophores, this study focused on the development of aminochalcone-based compounds, organic molecules characterized by a chalcone backbone (consisting of two aromatic rings connected by a three-carbon α,β-unsaturated carbonyl system) with an amino group attached to one of the aromatic rings, as potential neuroprotective agents. Thus, the aminochalcone-based compounds in this study were designed by bearing a -OCH3 moiety at different positions on the ring and synthesized by the Claisen-Schmidt condensation. The compounds exhibited strong neuroprotective effects against hydrogen peroxide-induced neuronal death in the human neuroblastoma (SH-SY5Y) cell line (i.e., by improving cell survival, reducing reactive oxygen species production, maintaining mitochondrial function, and preventing cell membrane damage). The aminochalcone-based compounds showed mild toxicity toward a normal embryonic lung cell line (MRC-5) and a human neuroblastoma cell line, and were predicted to have preferable pharmacokinetic profiles with potential for oral administration. Molecular docking simulation indicated that the studied aminochalcones may act as competitive activators of the well-known protective protein, SIRT1, and provided beneficial knowledge regarding the essential key chemical moieties and interacting amino acid residues. Collectively, this work provides a series of four promising candidate agents that could be developed for neuroprotection.

Metabolic Priming as a Tool in Redox and Mitochondrial Theragnostics

Theragnostics is a promising approach that integrates diagnostics and therapeutics into a single personalized strategy. To conduct effective theragnostic studies, it is essential to create an in vitro environment that accurately reflects the in vivo conditions. In this review, we discuss the importance of redox homeostasis and mitochondrial function in the context of personalized theragnostic approaches. Cells have several ways to respond to metabolic stress, including changes in protein localization, density, and degradation, which can promote cell survival. However, disruption of redox homeostasis can lead to oxidative stress and cellular damage, which are implicated in various diseases. Models of oxidative stress and mitochondrial dysfunction should be developed in metabolically conditioned cells to explore the underlying mechanisms of diseases and develop new therapies. By choosing an appropriate cellular model, adjusting cell culture conditions and validating the cellular model, it is possible to identify the most promising therapeutic options and tailor treatments to individual patients. Overall, we highlight the importance of precise and individualized approaches in theragnostics and the need to develop accurate in vitro models that reflect the in vivo conditions.

Fabrication of a Low-Cost Microfluidic Device for High-Throughput Drug Testing on Static and Dynamic Cancer Spheroid Culture Models

Drug development is a complex and expensive process from new drug discovery to product approval. Most drug screening and testing rely on in vitro 2D cell culture models; however, they generally lack in vivo tissue microarchitecture and physiological functionality. Therefore, many researchers have used engineering methods, such as microfluidic devices, to culture 3D cells in dynamic conditions. In this study, a simple and low-cost microfluidic device was fabricated using Poly Methyl Methacrylate (PMMA), a widely available material, and the total cost of the completed device was USD 17.75. Dynamic and static cell culture examinations were applied to monitor the growth of 3D cells. α-MG-loaded GA liposomes were used as the drug to test cell viability in 3D cancer spheroids. Two cell culture conditions (i.e., static and dynamic) were also used in drug testing to simulate the effect of flow on drug cytotoxicity. Results from all assays showed that with the velocity of 0.005 mL/min, cell viability was significantly impaired to nearly 30% after 72 h in a dynamic culture. This device is expected to improve in vitro testing models, reduce and eliminate unsuitable compounds, and select more accurate combinations for in vivo testing.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations

Artificial Intelligence (AI) emerged as an intervention for data and number-related problems. This breakthrough has led to several technological advancements in virtually all fields from engineering to architecture, education, accounting, business, health, and so on. AI has come a long way in healthcare, having played significant roles in data and information storage and management - such as patient medical histories, medicine stocks, sale records, and so on; automated machines; software and computer applications like diagnostic tools such as MRI radiation technology, CT diagnosis and many more have all been created to aid and simplify healthcare measures. Inarguably, AI has revolutionized healthcare to be more effective and efficient and the pharmacy sector is not left out. During the past few years, a considerable amount of increasing interest in the uses of AI technology has been identified for analyzing as well as interpreting some important fields of pharmacy like drug discovery, dosage form designing, polypharmacology, and hospital pharmacy. Given the growing importance of AI, we wanted to create a comprehensive report which helps every practicing pharmacist understand the biggest breakthroughs which are assisted by the deployment of this field.

Altering gene expression using antisense oligonucleotide therapy for hearing loss

Hearing loss affects more than 430 million people, worldwide, and is the third most common chronic physical condition in the United States and Europe (GBD Hearing Loss Collaborators, 2021; NIOSH, 2021; WHO, 2021). The loss of hearing significantly impacts motor and cognitive development, communication, education, employment, and overall quality of life. The inner ear houses the sensory organs for both hearing and balance and provides an accessible target for therapeutic delivery. Antisense oligonucleotides (ASOs) use various mechanisms to manipulate gene expression and can be tailor-made to treat disorders with defined genetic targets. In this review, we discuss the preclinical advancements within the field of the highly promising ASO-based therapies for hereditary hearing loss disorders. Particular focus is on ASO mechanisms of action, preclinical studies on ASO treatments of hearing loss, timing of therapeutic intervention, and delivery routes to the inner ear.

Integrating an aerosolized drug delivery device with conventional static cultures and a dynamic airway barrier microphysiological system

Organ on a chip or microphysiological systems (MPSs) aim to resolve current challenges surrounding drug discovery and development resulting from an unrepresentative static cell culture or animal models that are traditionally used by generating a more physiologically relevant environment. Many different airway MPSs have been developed that mimic alveolar or bronchial interfaces, but few methods for aerosol drug delivery at the air-liquid interface exist. This work demonstrates a compact Surface Acoustic Wave (SAW) drug delivery device that generates an aerosol of respirable size for delivery of compounds directly onto polarized or differentiated epithelial cell cultures within an airway barrier MPS and conventional static inserts. As proof of principle, the SAW drug delivery device was used to nebulize viral dsRNA analog poly I:C and steroids fluticasone and dexamethasone without disrupting their biological function.

Screening for Potential Therapeutic Agents for Non-Small Cell Lung Cancer by Targeting Ferroptosis

Ferroptosis is a form of non-apoptotic and iron-dependent cell death originally identified in cancer cells. Recently, emerging evidence showed that ferroptosis-targeting therapy could be a novel promising anti-tumour treatment. However, systematic analyses of ferroptosis-related genes for the prognosis of non-small cell lung cancer (NSCLC) and the development of antitumor drugs exploiting the ferroptosis process remain rare. This study aimed to identify genes related to ferroptosis and NSCLC and to initially screen lead compounds that induce ferroptosis in tumor cells. We downloaded mRNA expression profiles and NSCLC clinical data from The Cancer Genome Atlas database to explore the prognostic role of ferroptosis-related genes. Four prognosis-associated ferroptosis-related genes were screened using univariate Cox regression analysis and the lasso Cox regression analysis, which could divide patients with NSCLC into high- and low-risk groups. Then, based on differentially expressed risk- and ferroptosis-related genes, the negatively correlated lead compound flufenamic acid (FFA) was screened through the Connective Map database. This project confirmed that FFA induced ferroptosis in A549 cells and inhibited growth and migration in a dose-dependent manner through CCK-8, scratch, and immunofluorescence assays. In conclusion, targeting ferroptosis might be a therapeutic alternative for NSCLC.

Study of the metabolic stability profiles of perampanel, rufinamide and stiripentol and prediction of drug interactions using HepaRG cells as an in vitro human model

New-generation antiepileptic drugs as perampanel, rufinamide and stiripentol emerged as alternatives in chronic epilepsy polytherapy. Hence, their metabolic stability and potential involvement in relevant drug-drug interactions (DDI) are of great clinical interest, being HepaRG cells herein used as an in vitro human model. To characterize their metabolic stability profiles, HepaRG cells were incubated with perampanel (1 μM), rufinamide (100 μM) or stiripentol (5 μM) for 12-h. HepaRG cells, pretreated with known CYP450 isoenzymes inducers (rifampicin, phenytoin, phenobarbital, omeprazole and carbamazepine), were also incubated with perampanel, rufinamide or stiripentol to assess possible DDI mediated by CYP induction. Results suggest a considerable decrease in perampanel and stiripentol concentrations over 12-h; contrary, rufinamide concentrations did not variated. Cells pretreatment with all inducers significantly decreased stiripentol concentrations (between 20.3% and 31.9%), suggesting a considerable potential for DDI. Rufinamide concentrations only decreased when preincubated with rifampicin and with the highest tested concentrations of the remaining inducers. Perampanel levels decreased with rifampicin, carbamazepine and phenobarbital, supporting the involvement of CYP3A4-mediated metabolism. Besides relevant information concerning the metabolic stability profile and potential DDIs of the new antiepileptics here studied, it was also reinforced the HepaRG cells suitability as a reliable in vitro model to foresee in vivo metabolism in humans.

Metabolomics of clinical samples reveal the treatment mechanism of lanthanum hydroxide on vascular calcification in chronic kidney disease

Previous studies showed that lanthanum hydroxide (LH) has a therapeutic effect on chronic kidney disease (CKD) and vascular calcification, which suggests that it might have clinical value. However, the target and mechanism of action of LH are unclear. Metabolomics of clinical samples can be used to predict the mechanism of drug action. In this study, metabolomic profiles in patients with end-stage renal disease (ESRD) were used to screen related signaling pathways, and we verified the influence of LH on the ROS-PI3K-AKT-mTOR-HIF-1α signaling pathway by western blotting and quantitative real-time RT-qPCR in vivo and in vitro. We found that ROS and SLC16A10 genes were activated in patients with ESRD. The SLC16A10 gene is associated with six significant metabolites (L-cysteine, L-cystine, L-isoleucine, L-arginine, L-aspartic acid, and L-phenylalanine) and the PI3K-AKT signaling pathway. The results showed that LH inhibits the ESRD process and its cardiovascular complications by inhibiting the ROS-PI3K-AKT-mTOR-HIF-1α signaling pathway. Collectively, LH may be a candidate phosphorus binder for the treatment of vascular calcification in ESRD.

Use of real-world evidence in translational pharmacology research

Real-world evidence (RWE) refers to observational data gathered outside the formalism of randomized controlled trials, in real life situations, on marketed drugs. While clinical trials are the gold standards to demonstrate the efficacy and tolerability of a medicinal product, the generalizability of their results to actual use in real-life is limited by the biases induced by the very nature of clinical trials; indeed, the patients included in the trials may differ from actual users because of their concomitant diseases or treatments, or other factors excluding them from the trials. Clinical researchers and pharmaceutical industries have hence become increasingly interested in expanding and integrating RWE into clinical research, by capitalizing on the exponential growth in access to data from electronic health records, claims databases, electronic devices, software or mobile applications, registries embedded in clinical practice and social media. Meanwhile, applications of RWE may also be used for drug discovery and repurposing, for clinical developments and post-marketing studies. The aim of this review is to provide our opinion regarding the use of RWE in translational research, including non-clinical and clinical pharmacology research, at the different step of drugs development use.

Recent applications of quantitative systems pharmacology and machine learning models across diseases

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

Performance of Affinity-Improved DARPin Targeting HIV Capsid Domain in Interference of Viral Progeny Production

Previously, a designed ankyrin repeat protein, Ank^GAG1D4, was generated for intracellular targeting of the HIV-1 capsid domain. The efficiency was satisfactory in interfering with the HIV assembly process. Consequently, improved Ank^GAG1D4 binding affinity was introduced by substituting tyrosine (Y) for serine (S) at position 45. However, the intracellular anti-HIV-1 activity of Ank^GAG1D4-S45Y has not yet been validated. In this study, the performance of Ank^GAG1D4 and Ank^GAG1D4-S45Y in inhibiting wild-type HIV-1 and HIV-1 maturation inhibitor-resistant replication in SupT1 cells was evaluated. HIV-1 p24 and viral load assays were used to verify the biological activity of Ank^GAG1D4 and Ank^GAG1D4-S45Y as assembly inhibitors. In addition, retardation of syncytium formation in infected SupT1 cells was observed. Of note, the defense mechanism of both ankyrins did not induce the mutation of target amino acids in the capsid domain. The present data show that the potency of Ank^GAG1D4-S45Y was superior to Ank^GAG1D4 in interrupting either HIV-1 wild-type or the HIV maturation inhibitor-resistant strain.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Emerging drugs for the treatment of myasthenia gravis

Introduction: Advances in understanding the immune pathomechanisms in myasthenia gravis (MG) allow for the development of novel targeted immune therapies. By working at specific points in the immunopathogenesis, these agents have the potential to provide rapid and efficacious responses compared to conventional immunosuppressive therapy (IST), addressing unmet needs and consequently are a research priority.Areas covered: This paper reviews the advances in MG treatment modalities with their scientific rationale. A search of clinicaltrials.gov and a literature search of PubMed from January 2015 to the end of June 2021 was done using the search terms: MG, treatment, immune targets to obtain information on recent developments of complement inhibitors, FcRn receptor inhibitors, direct and indirect B cell inhibitors, CAR and CAAR- T cell therapy, and hematopoietic stem cell transplantation. Specific agents in various phases of clinical development, evidence from ongoing trials and potential roadblocks are examined.Expert opinion: Despite several promising novel agents, existing data as to the timing of initiation and duration of treatment, long-term safety profile and utility in certain patient subsets are limited and require further research. Despite these considerations, the future of MG treatment is transitioning from broad-spectrum IST toward precise, target-driven and personalized immunotherapy.

Who funds Alzheimer's disease drug development?

INTRODUCTION

Despite the increase in Alzheimer's disease (AD) cases in the United States, no new treatments have been approved in the United States since 2003. The costs associated with drug development programs are high and serve as a significant deterrent to AD therapeutic investigations. In this study, we analyze the sponsorship data for AD clinical trials conducted since 2016 to assess the fiscal support for AD clinical trials.

METHODS

We analyzed the funding sources of all AD trials over the past 5 years as reported on ClinicalTrials.gov.

RESULTS

There were 136 trials being conducted for treatments in the US AD therapeutic pipeline on the index date of this study. Among non-prevention trials, disease-modifying therapies (DMT) in Phase 3 were almost entirely sponsored by the biopharmaceutical industry; Phase 2 DMT trials were split between the biopharmaceutical industry and funding from the National Institutes of Health (NIH) to academic medical centers (AMCs). The majority of prevention trials received sponsorship from public-private partnerships (PPP). Trials of symptomatic agents are equally likely to have biopharmaceutical or NIH/AMC sponsorship. Most trials with repurposed agents had NIH/AMC funding (89%). Since 2016, there has been consistent growth in the number of trials sponsored both in part and fully by NIH/AMC sources and in PPP, and there has been a reduction in biopharmaceutical company-sponsored trials.

DISCUSSION

The number of trials supported by the biopharmaceutical industry has decreased over the past 5 years; trials supported from federal sources and PPP have increased. Repurposed compounds are mostly in Phase 2 trials and provide critical mechanistic information.

KRAS and EGFR Mutations Differentially Alter ABC Drug Transporter Expression in Cisplatin-Resistant Non-Small Cell Lung Cancer

Lung carcinoma is still the most common malignancy worldwide. One of the major subtypes of non-small cell lung cancer (NSCLC) is adenocarcinoma (AC). As driver mutations and hence therapies differ in AC subtypes, we theorized that the expression and function of ABC drug transporters important in multidrug resistance (MDR) would correlate with characteristic driver mutations KRAS or EGFR. Cisplatin resistance (CR) was generated in A549 (KRAS) and PC9 (EGFR) cell lines and gene expression was tested. In three-dimensional (3D) multicellular aggregate cultures, both ABCB1 and ABCG2 transporters, as well as the WNT microenvironment, were investigated. ABCB1 and ABCG2 gene expression levels were different in primary AC samples and correlated with specific driver mutations. The drug transporter expression pattern of parental A549 and PC9, as well as A549-CR and PC9-CR, cell lines differed. Increased mRNA levels of ABCB1 and ABCG2 were detected in A549-CR cells, compared to parental A549, while the trend observed in the case of PC9 cells was different. Dominant alterations were observed in LEF1, RHOU and DACT1 genes of the WNT signalling pathway in a mutation-dependent manner. The study confirmed that, in lung AC-s, KRAS and EGFR driver mutations differentially affect both drug transporter expression and the cisplatin-induced WNT signalling microenvironment.

Drug development post COVID-19 pandemic: toward a better system to meet current and future global health challenges

Introduction: Despite advances in drug research and development, our knowledge of the underlying molecular mechanisms of many diseases remains inadequate. This have led to limited effective medicines for several diseases. To address these challenges, efficient strategies, novel technologies, and policies are urgently needed. The main obstacles in drug discovery and development are the mounting cost, risk, and time frame needed to develop new medicines. Fair pricing and accessibility is another unmet global challenge.Areas covered: Here, the authors cover the pace, risks, cost, and challenges facing drug development processes. Additionally, they introduce disease-associated data which demand global attention and propose solutions to overcome these challenges.Expert opinion: The massive challenges encountered during drug development urgently call for a serious global rethinking of the way this process is done. A partial solution might be if many consortiums of multi-nations, academic institutions, clinicians, pharma companies, and funding agencies gather at different fronts to crowdsource resources, share knowledge and risks. Such an ecosystem can rapidly generate first-in-class molecules that are safe, effective, and affordable. We think that this article represents a wake-up call for the scientific community to immediately reassess the current drug discovery and development procedures.

Driving success in personalized medicine through AI-enabled computational modeling

The development of successful drugs is expensive and time-consuming because of high clinical attrition rates. This is caused partially by the rupture seen in the translatability of the drug from the bench to the clinic in the context of personalized medicine. Artificial intelligence (AI)-driven platforms integrated with mechanistic modeling have become instrumental in accelerating the drug development process by leveraging data ubiquitously across the various phases. AI can counter the deficiencies and ambiguities that arise during the classical drug development process while reducing human intervention and bridging the translational gap in discovering the connections between drugs and diseases.

Drug-drug similarity measure and its applications

Drug similarities play an important role in modern biology and medicine, as they help scientists gain deep insights into drugs' therapeutic mechanisms and conduct wet labs that may significantly improve the efficiency of drug research and development. Nowadays, a number of drug-related databases have been constructed, with which many methods have been developed for computing similarities between drugs for studying associations between drugs, human diseases, proteins (drug targets) and more. In this review, firstly, we briefly introduce the publicly available drug-related databases. Secondly, based on different drug features, interaction relationships and multimodal data, we summarize similarity calculation methods in details. Then, we discuss the applications of drug similarities in various biological and medical areas. Finally, we evaluate drug similarity calculation methods with common evaluation metrics to illustrate the important roles of drug similarity measures on different applications.

Engineered tissues and strategies to overcome challenges in drug development

Current preclinical studies in drug development utilize high-throughput in vitro screens to identify drug leads, followed by both in vitro and in vivo models to predict lead candidates' pharmacokinetic and pharmacodynamic properties. The goal of these studies is to reduce the number of lead drug candidates down to the most likely to succeed in later human clinical trials. However, only 1 in 10 drug candidates that emerge from preclinical studies will succeed and become an approved therapeutic. Lack of efficacy or undetected toxicity represents roughly 75% of the causes for these failures, despite these parameters being the primary exclusion criteria in preclinical studies. Recently, advances in both biology and engineering have created new tools for constructing new preclinical models. These models can complement those used in current preclinical studies by helping to create more realistic representations of human tissues in vitro and in vivo. In this review, we describe current preclinical models to identify their value and limitations and then discuss select areas of research where improvements in preclinical models are particularly needed to advance drug development. Following this, we discuss design considerations for constructing preclinical models and then highlight recent advances in these efforts. Taken together, we aim to review the advances as of 2020 surrounding the prospect of biological and engineering tools for adding enhanced biological relevance to preclinical studies to aid in the challenges of failed drug candidates and the burden this poses on the drug development enterprise and thus healthcare.

Drug targets for COVID-19 therapeutics: Ongoing global efforts

The current global pandemic COVID-19 caused by the SARS-CoV-2 virus has already inflicted insurmountable damage both to the human lives and global economy. There is an immediate need for identification of effective drugs to contain the disastrous virus outbreak. Global efforts are already underway at a war footing to identify the best drug combination to address the disease. In this review, an attempt has been made to understand the SARS-CoV-2 life cycle, and based on this information potential druggable targets against SARS-CoV-2 are summarized. Also, the strategies for ongoing and future drug discovery against the SARS-CoV-2 virus are outlined. Given the urgency to find a definitive cure, ongoing drug repurposing efforts being carried out by various organizations are also described. The unprecedented crisis requires extraordinary efforts from the scientific community to effectively address the issue and prevent further loss of human lives and health.

Exploring different approaches to improve the success of drug discovery and development projects: a review

Background

There has been a significant increase in the cost and timeline of delivering new drugs for clinical use over the last three decades. Despite the increased investments in research infrastructure by pharmaceutical companies and technological advances in the scientific tools available, efforts to increase the number of molecules coming through the drug development pipeline have largely been unfruitful.

Main body

A non-systematic review of the current literature was undertaken to enumerate the various strategies employed to improve the success rates in the pharmaceutical research and development. The review covers the exploitation of genomics and proteomics, complementarity of target-based and phenotypic efficacy screening platforms, drug repurposing and repositioning, collaborative research, focusing on underserved therapeutic fields, outsourcing strategy, and pharmaceutical modeling and artificial intelligence. Examples of successful drug discoveries achieved through application of these strategies are highlighted and discussed herein.

Conclusions

Genomics and proteomics have uncovered a wide array of potential drug targets and are facilitative of enhanced scrupulous target identification and validation thus reducing efficacy-related drug attrition. When used complementarily, phenotypic and target-based screening platforms would likely allow serendipitous drug discovery while increasing rationality in drug design. Drug repurposing and repositioning reduces financial risks in drug development accompanied by cost and time savings, while prolonging patent exclusivity hence increased returns on investment to the innovator company. Equally important, collaborative research is facilitative of cross-fertilization and refinement of ideas, while sharing resources and expertise, hence reducing overhead costs in the early stages of drug discovery. Underserved therapeutic fields are niche drug discovery areas that may be used to experiment and launch novel drug targets, while exploiting incentivized benefits afforded by drug regulatory authorities. Outsourcing allows the pharma industries to focus on their core competencies while deriving greater efficiency of specialist contract research organizations. The existing and emerging pharmaceutical modeling and artificial intelligence softwares and tools allow for in silico computation enabling more efficient computer-aided drug design. Careful selection and application of these strategies, singly or in combination, may potentially harness pharmaceutical research and innovation.

Partnerships for better neglected disease drug discovery and development: how have we fared?

Introduction: In the field of neglected disease, mushrooming partnerships have changed the landscape in the last decades. With high diversity in participants, type, scope, and operational models, partnership becomes the ultimate choice for drug discovery and development. This paper aims to reflect on this phenomenon based on experiences and lessons learned, providing insights for the future.Areas covered: Lack of safe and effective drugs for neglected diseases stems from market and public policy failure. Combining resources, skills, and expertise justifies working collaboratively in the R&D quest. The advancement of public-private partnerships (PPP), including product development partnership (PDP) for neglected diseases, is described, herein, including the rationale behind their conception, evolution, expansion, and alternative approaches. The author also discusses the appeals and the pitfalls of partnership in this field.Expert opinion: The progressive partnerships in drug discovery and development for neglected diseases need to be encouraged, especially in alignment with an open science culture. Experiences in partnerships vary with bias for successful ones, rendering more rigorous evaluation and research necessary. Eventually, the focus of improving partnership should not only be on addressing discovery bottlenecks, but also safeguarding access and delivery. Expanding focus to include vaccines and diagnostics is necessary.

Nanobodies: a new approach for the diagnosis and treatment of viral infectious diseases

With the rise of viral infections and antibiotic resistance, there is a constant need for the development of more sensitive and effective treatment and diagnostic tools. Since their discovery in the early 1990s, Camelidae antibodies have been investigated as potential tools due to their unique structure and favorable characteristics. Members of this family produce conventional IgG antibodies as well as heavy-chain only IgG antibodies that do not possess light chains. The variable domain (VHH), or nanobody, demonstrates unique antigen-binding capabilities, enhanced stability, and its small size allows for delivery into the body using a nebulizer, thereby eliminating the unfavorable use of injections. In addition, the cost-effective and easy in vitro production of these antibodies are an attractive quality in terms of mass production. This review covers the past and current nanobody treatment and diagnostic developments aimed at viral infectious diseases, including a brief overview of protozoal, bacterial, and veterinary viral approaches.

Chalcones, stilbenes and ketones have anti-infective properties via inhibition of bacterial drug-efflux and consequential synergism with antimicrobial agents

With antimicrobial resistance creating a major public health crisis, the designing of novel antimicrobial compounds that effectively combat bacterial infection is becoming increasingly critical. Interdisciplinary approaches integrate the best features of whole-cell phenotypic evaluation to validate novel therapeutic targets and discover new leads to combat antimicrobial resistance. In this project, whole-cell phenotypic evaluation such as testing inhibitors on bacterial growth, viability, efflux pump, biofilm formation and their interaction with other drugs were performed on a panel of Gram-positive, Gram-negative and acid-fast group of bacterial species. This enabled additional antimicrobial activities of compounds belonging to the flavonoid family including ketones, chalcones and stilbenes, to be identified. Flavonoids have received renewed attention in literature over the past decade, and a variety of beneficial effects of these compounds have been illuminated, including anti-cancer, anti-inflammatory, anti-tumour as well as anti-fungal and anti-bacterial. However, their mechanisms of action are yet to be identified. In this paper, we found that the compounds belonging to the flavonoid family exerted a range of anti-infective properties being identified as novel efflux pump inhibitors, whilst offering the opportunity to be used in combination therapy. The compound 2-phenylacetophenone displayed broad-spectrum efflux pump inhibition activity, whilst trans-chalcone, displayed potent activity against Gram-negative and mycobacterial efflux pumps causing inhibition higher than known potent efflux pump inhibitors, verapamil and chlorpromazine. Drug-drug interaction studies also highlighted that 2-phenylacetophenone not only has the potential to work additively with known antibacterial agents that affect the cell-wall and DNA replication but also trans-chalcone has the potential to work synergistically with anti-tubercular agents. Overall, this paper shows how whole-cell phenotypic analysis allows for the discovery of new antimicrobial agents and their consequent mode of action whilst offering the opportunity for compounds to be repurposed, in order to contribute in the fight against antimicrobial resistance.

Factors influencing participation dynamics in research for development interventions with multi-stakeholder platforms: A metric approach to studying stakeholder participation

Multi-stakeholder platforms have become mainstream in projects, programmes and policy interventions aiming to improve innovation and livelihoods systems, i.e. research for development interventions in low- and middle-income contexts. However, the evidence for multi-stakeholder platforms’ contribution to the performance of research for development interventions and their added value is not compelling. This paper focuses on stakeholder participation as one of the channels for multi-stakeholder platforms’ contribution to the performance of research for development interventions, i.e. stakeholder participation. It uses a quantitative approach and utilizes descriptive statistics and ARIMA models. It shows that, in three Ugandan multi-stakeholder platform cases studied, participation increased both in nominal and in unique terms. Moreover, participation was rather cyclical and fluctuated during the implementation of the research for development interventions. The study also shows that, in addition to locational and intervention factors such as type of the area along a rural–urban gradient targeted by the intervention and human resources provided for multi-stakeholder platform implementation, temporal elements such as phases of research for development intervention objectives and the innovation development process play significant roles in influencing participation. The study concludes that contribution of multi-stakeholder platforms to the performance of research for development projects, programs, policies and other initiatives is constrained by locational and temporal context and conditional on the participation requirements of the objectives pursued by research for development intervention.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Typological analysis of public-private partnerships in the veterinary domain

Public-Private Partnerships (PPPs) are defined as a collaborative approach in which the public and private sector share resources, responsibilities and risks to achieve common objectives and mutual benefits in a sustainable manner. PPPs are identified as a key solution to reinforce Veterinary Services. However only limited information is available on the scope, added value and enabling factors of PPPs in this sector. The aims of this study were to develop a typology of PPPs in the veterinary field and to identify key success factors and obstacles to their implementation. A structured questionnaire was sent to all 181 World Organisation for Animal Health (OIE) Member Countries and to 47 private contacts. 36 different variables characterizing PPP initiatives were collected. 97 examples of PPPs were retrieved from 76 countries. Dimensionality reduction techniques were combined with clustering and discrimination methods to establish a typology of PPPs and to derive a set of simple rules to classify new instances of PPPs. Three clusters were identified, separated according to two main variables: the type of private partners and the type of interaction. Cluster 1, transactional PPPs, represented the traditional understanding of PPPs by Veterinary Services, initiated and funded by the public sector, giving service delivery accreditation to mostly private veterinarians; cluster 2, collaborative PPPs, included partnerships between producer associations and public Veterinary Services, driven by trade interests; cluster 3, transformational PPPs, represented joint programs initiated and funded by private companies and initially driven by business development objectives. Specific success factors and key obstacles affecting the performances and sustainability of these initiatives were identified for each cluster. This study represents the first practical attempt to develop a meaningful typology of PPPs in the field of animal health and to identify fundamental obstacles currently inhibiting the development of PPPs, and suggests ways to support national Veterinary Services in overcoming these obstacles.

Identifying Frameworks for Validation and Monitoring of Consensual Behavioral Intervention Technologies: Narrative Review

BACKGROUND

Changing health behaviors, such as smoking, unhealthy eating, inactivity, and alcohol abuse, may have a greater impact on population health than any curative strategy. One of the suggested strategies is the use of behavioral intervention technologies (BITs). They open up new opportunities in the area of prevention and therapy and have begun to show benefits in the durable change of health behaviors in patients or those at risk. A consensual and international paradigm was adopted by health authorities for drugs 50 years ago. It guides their development from research units to their authorization and surveillance. BITs' generalization brings into question their upstream evaluation before being placed on the market and their downstream monitoring once on the market; this is especially the case in view of the marketing information provided by manufacturers and the scarcity and methodological limits of scientific studies on these tools.

OBJECTIVE

This study aims to identify and categorize the frameworks for the validation and monitoring of BITs proposed in the literature.

METHODS

We conducted a narrative literature review using MEDLINE, PsycINFO, and Web of Science. The review items included the following: name, publication year, name of the creator (ie, first author), country, funding organization, health focus, target group, and design (ie, linear, iterative, evolutive, and/or concurrent). The frameworks were then categorized based on (1) translational research thanks to a continuum of steps and (2) the three paradigms that may have inspired the frameworks: biomedical, engineering, and/or behavioral.

RESULTS

We identified 46 frameworks besides the classic US Food and Drug Administration (FDA) five-phase drug development model. A total of 57% (26/46) of frameworks were created in the 2010s and 61% (28/46) involved the final user in an early and systematic way. A total of 4% (2/46) of frameworks had a linear-only sequence of their phases, 37% (17/46) had a linear and iterative structure, 33% (15/46) added an evolutive structure, and 24% (11/46) were associated with a parallel process. Only 12 out of 46 (26%) frameworks covered the continuum of steps and 12 (26%) relied on the three paradigms.

CONCLUSIONS

To date, 46 frameworks of BIT validation and surveillance coexist, besides the classic FDA five-phase drug development model, without the predominance of one of them or convergence in a consensual model. Their number has increased exponentially in the last three decades. Three dangerous scenarios are possible: (1) anarchic continuous development of BITs that depend on companies amalgamating health benefits and usability (ie, user experience, data security, and ergonomics) and limiting implementation to several countries; (2) the movement toward the type of framework for drug evaluation centered on establishing its effectiveness before marketing authorization to guarantee its safety for users, which is heavy and costly; and (3) the implementation of a framework reliant on big data analysis based on a posteriori research and an autoregulation of a market, but that does not address the safety risk for the health user, as the market will not regulate safety or efficacy issues. This paper recommends convergence toward an international validation and surveillance framework based on the specificities of BITs, not equivalent to medical devices, to guarantee their effectiveness and safety for users.

Big Data and Public-Private Partnerships in Healthcare and Research: The Application of an Ethics Framework for Big Data in Health and Research

Public-private partnerships (PPPs) are established to specifically harness the potential of Big Data in healthcare and can include partners working across the data chain-producing health data, analysing data, using research results or creating value from data. This domain paper will illustrate the challenges that arise when partners from the public and private sector collaborate to share, analyse and use biomedical Big Data. We discuss three specific challenges for PPPs: working within the social licence, public antipathy to the commercialisation of public sector health data, and questions of ownership, both of the data and any resulting intellectual property or products. As a specific example we consider the case of the UK National Health Service (NHS) providing patient data to Google's DeepMind AI program to develop a diagnostic app for kidney disease. This article is an application of the framework presented in this issue of ABR (Xafis et al. 2019). Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. We use four specific values to help analysis these issues: public benefit, stewardship, transparency and engagement. We demonstrate how the Deliberative Framework can support ethical governance of PPPs involving biomedical big data.

Short-term 3D culture systems of various complexity for treatment optimization of colorectal carcinoma

Three-dimensional (3D) cultures have the potential to increase the predictive value of pre-clinical drug research and bridge the gap towards anticipating clinical outcome of proposed treatments. However, their implementation in more advanced drug-discovery programs is still in its infancy due to the lack of reproducibility and low time- and cost effectiveness. HCT116, SW620 and DLD1 cells, cell lines with distinct mutations, grade and origin, were co-cultured with fibroblasts and endothelial cells (EC) in 3D spheroids. Clinically relevant drugs, i.e. 5-fluorouracil (5−FU), regorafenib and erlotinib, were administered individually to in CRC cell cultures. In this study, we established a robust, low-cost and reproducible short-term 3D culture system addressing the various complexities of the colorectal carcinoma (CRC) microenvironment. We observed a dose-dependent increase of erlotinib sensitivity in 3D (co-)cultures compared to 2D cultures. Furthermore, we compared the drug combination efficacy and drug-drug interactions administered in 2D, 3D and 3D co-cultures. We observed that synergistic/additive drug-drug interactions for drug combinations administered at low doses shifted towards additive and antagonistic when applied at higher doses in metastatic CRC cells. The addition of fibroblasts at various ratios and EC increased the resistance to some drug combinations in SW620 and DLD1 cells, but not in HCT116. Retreatment of SW620 3D co-cultures with a low-dose 3-drug combination was as active (88% inhibition, relative to control) as 5-FU treatment at high dose (100 μM). Moreover, 3D and 3D co-cultures responded variably to the drug combination treatments, and also signalling pathways were differently regulated, probably due to the influence of fibroblasts and ECs on cancer cells. The short-term 3D co-culture system developed here is a powerful platform for screening (combination) therapies. Understanding of signalling in 3D co-cultures versus 3D cultures and the responses in the 3D models upon drug treatment might be beneficial for designing anti-cancer therapies.

Why Are New Drugs Expensive and How Can They Stay Affordable?

Increasing life expectancy leading to a higher median age causes an increasing need for healthcare resources, which is aggravated by an increasing prevalence of preventable diseases such as type 2 diabetes. This includes increasing expenditures for medicines, although these increases when expressed as a share of overall societal wealth are more moderate than often claimed. An increasing use of generic medicines (currently about 90% of all prescriptions) means that costs for discovery and development of innovative drugs must be recovered on a shrinking percentage of prescriptions. However, the key challenge to affordable drugs is exponentially increasing costs to bring a new medicine to the market, which in turn are largely driven by an about 90% attrition rate after start of clinical development. While many factors will be required in concert to keep innovative medicines affordable, reducing attrition appears to be the factor with the greatest potential to contain escalating drug development costs and thereby medication expenditures.

Developing a clinical trial governance framework for pharmaceutical industry-funded clinical trials

Rising concerns relating to pharmaceutical sponsor bias in the conduct of clinical trials have compelled the need to develop a clinical trial governance framework. This article describes the development of the Conflict of Interest in Research (COIR), a clinical trial governance framework. The COIR, consisting of three process phases (initiation, concurrent, and ongoing), developed following a needs assessment, using a four-stage methodology, and evaluated against the International Conference on Harmonization--Good Clinical Practice (ICH-GCP) guidelines. The Conflict of Interest Resolution algorithm, the backbone of COIR, enables constant surveillance to detect/resolve conflicts at all stages of the clinical trial life-cycle. COIR promotes interaction between the regulatory system and the sponsors, independent of individuals. COIR enables rapid detection of scientific and financial conflicts, to prevent subject harm and, to assure optimal funds utilization, the latter feature helped to reduce a significant burden for the ethics committee, as it lacks financial expertise. COIR is a semi-automated Oracle system, requires manpower, and is affected by human expertise and subjectivity. Complete automation to overcome this limitation will still need human expertise to scale changing trial regulations. Nevertheless, the COIR has won the distinction of the "most favored site" from pharmaceutical sponsors and is anticipated to be adopted by other clinical trial sites.

Real world big data for clinical research and drug development

The objective of this paper is to identify the extent to which real world data (RWD) is being utilized, or could be utilized, at scale in drug development. Through screening peer-reviewed literature, we have cited specific examples where RWD can be used for biomarker discovery or validation, gaining a new understanding of a disease or disease associations, discovering new markers for patient stratification and targeted therapies, new markers for identifying persons with a disease, and pharmacovigilance. None of the papers meeting our criteria was specifically geared toward novel targets or indications in the biopharmaceutical sector; the majority were focused on the area of public health, often sponsored by universities, insurance providers or in combination with public health bodies such as national insurers. The field is still in an early phase of practical application, and is being harnessed broadly where it serves the most direct need in public health applications in early, rare and novel disease incidents. However, these exemplars provide a valuable contribution to insights on the use of RWD to create novel, faster and less invasive approaches to advance disease understanding and biomarker discovery. We believe that pharma needs to invest in making better use of Electronic Health Records and the need for more precompetitive collaboration to grow the scale of this 'big denominator' capability, especially given the needs of precision medicine research.

The RA-MAP Consortium: a working model for academia-industry collaboration

Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world.

Giving Drugs a Second Chance: Overcoming Regulatory and Financial Hurdles in Repurposing Approved Drugs As Cancer Therapeutics

The repositioning or “repurposing” of existing therapies for alternative disease indications is an attractive approach that can save significant investments of time and money during drug development. For cancer indications, the primary goal of repurposed therapies is on efficacy, with less restriction on safety due to the immediate need to treat this patient population. This report provides a high-level overview of how drug developers pursuing repurposed assets have previously navigated funding efforts, regulatory affairs, and intellectual property laws to commercialize these “new” medicines in oncology. This article provides insight into funding programs (e.g., government grants and philanthropic organizations) that academic and corporate initiatives can leverage to repurpose drugs for cancer. In addition, we highlight previous examples where secondary uses of existing, Food and Drug Administration- or European Medicines Agency-approved therapies have been predicted in silico and successfully validated in vitro and/or in vivo (i.e., animal models and human clinical trials) for certain oncology indications. Finally, we describe the strategies that the pharmaceutical industry has previously employed to navigate regulatory considerations and successfully commercialize their drug products. These factors must be carefully considered when repurposing existing drugs for cancer to best benefit patients and drug developers alike.