How Much Does It Cost to Research and Develop a New Drug? A Systematic Review and Assessment

AbMelt: Learning antibody thermostability from molecular dynamics

Antibody thermostability is challenging to predict from sequence and/or structure. This difficulty is likely due to the absence of direct entropic information. Herein, we present AbMelt where we model the inherent flexibility of homologous antibody structures using molecular dynamics simulations at three temperatures and learn the relevant descriptors to predict the temperatures of aggregation (Tagg), melt onset (Tm,on), and melt (Tm). We observed that the radius of gyration deviation of the complementarity determining regions at 400 K is the highest Pearson correlated descriptor with aggregation temperature (rp = -0.68 ± 0.23) and the deviation of internal molecular contacts at 350 K is the highest correlated descriptor with both Tm,on (rp = -0.74 ± 0.04) as well as Tm (rp = -0.69 ± 0.03). Moreover, after descriptor selection and machine learning regression, we predict on a held-out test set containing both internal and public data and achieve robust performance for all endpoints compared with baseline models (Tagg R2 = 0.57 ± 0.11, Tm,on R2 = 0.56 ± 0.01, and Tm R2 = 0.60 ± 0.06). In addition, the robustness of the AbMelt molecular dynamics methodology is demonstrated by only training on <5% of the data and outperforming more traditional machine learning models trained on the entire data set of more than 500 internal antibodies. Users can predict thermostability measurements for antibody variable fragments by collecting descriptors and using AbMelt, which has been made available.

Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations

Active learning (AL) is a specific instance of sequential experimental design and uses machine learning to intelligently choose the next data point or batch of molecular structures to be evaluated. In this sense, it closely mimics the iterative design-make-test-analysis cycle of laboratory experiments to find optimized compounds for a given design task. Here, we describe an AL protocol which combines generative molecular AI, using REINVENT, and physics-based absolute binding free energy molecular dynamics simulation, using ESMACS, to discover new ligands for two different target proteins, 3CLpro and TNKS2. We have deployed our generative active learning (GAL) protocol on Frontier, the world's only exa-scale machine. We show that the protocol can find higher-scoring molecules compared to the baseline, a surrogate ML docking model for 3CLpro and compounds with experimentally determined binding affinities for TNKS2. The ligands found are also chemically diverse and occupy a different chemical space than the baseline. We vary the batch sizes that are put forward for free energy assessment in each GAL cycle to assess the impact on their efficiency on the GAL protocol and recommend their optimal values in different scenarios. Overall, we demonstrate a powerful capability of the combination of physics-based and AI methods which yields effective chemical space sampling at an unprecedented scale and is of immediate and direct relevance to modern, data-driven drug discovery.

Antineoplastics for treating Alzheimer's disease and dementia: Evidence from preclinical and observational studies

As the world population ages, there will be an increasing need for effective therapies for aging-associated neurodegenerative disorders, which remain untreatable. Dementia due to Alzheimer's disease (AD) is one of the leading neurological diseases in the aging population. Current therapeutic approaches to treat this disorder are solely symptomatic, making the need for new molecular entities acting on the causes of the disease extremely urgent. One of the potential solutions is to use compounds that are already in the market. The structures have known pharmacokinetics, pharmacodynamics, toxicity profiles, and patient data available in several countries. Several drugs have been used successfully to treat diseases different from their original purposes, such as autoimmunity and peripheral inflammation. Herein, we divulge the repurposing of drugs in the area of neurodegenerative diseases, focusing on the therapeutic potential of antineoplastics to treat dementia due to AD and dementia. We briefly touch upon the shared pathological mechanism between AD and cancer and drug repurposing strategies, with a focus on artificial intelligence. Next, we bring out the current status of research on the development of drugs, provide supporting evidence from retrospective, clinical, and preclinical studies on antineoplastic use, and bring in new areas, such as repurposing drugs for the prion-like spreading of pathologies in treating AD.

In silico modeling the potential clinical effect of growth factor treatment on the metabolism of human nucleus pulposus cells

Background

While growth factors have the potential to halt degeneration and decrease inflammation in animal models, the literature investigating the effect of dosage on human cells is lacking. Moreover, despite the completion of clinical trials using growth differentiation factor-5 (GDF-5), no results have been publicly released.

Aims

The overall objective was to quantitatively assess the effect of three clinically relevant concentrations of GDF-5 (0.25, 1, and 2 mg) as a therapeutic for disc regeneration.

Materials and methods

Firstly, this work experimentally determined the effects of GDF-5 concentration on the metabolic and matrix synthesis rates of human nucleus pulposus (NP) cells. Secondly, in silico modeling was employed to predict the subsequent regenerative effect of different GDF-5 treatments (± cells).

Results

This study suggests a trend of increased matrix synthesis with 0.25 and 1 mg of GDF-5. However, 2 mg of GDF-5 significantly upregulates oxygen consumption. Despite this, in silico models highlight the potential of growth factors in promoting matrix synthesis compared to cell-only treatments, without significantly perturbing the nutrient microenvironment.

Discussion

This work elucidates the potential of GDF-5 on human NP cells. Although the results did not reveal statistical differences across all doses, the variability and response among donors is an interesting finding. It highlights the complexity of human response to biological treatments and reinforces the need for further human research and personalized approaches. Furthermore, this study raises a crucial question about whether these potential biologics are more regenerative in nature or better suited as prophylactic therapies for younger patient groups.

Conclusion

Biological agents exhibit unique characteristics and features, demanding tailored development strategies and individualized assessments rather than a one-size-fits-all approach. Therefore, the journey to realizing the full potential of biological therapies is long and costly. Nonetheless, it holds the promise of revolutionizing spinal healthcare and improving the quality of life for patients suffering from discogenic back pain.

A new ciprofibrate calcium salt with improved solubility and intrinsic dissolution rate

Ciprofibrate (CIP) is an active pharmaceutical ingredient (API) classified as class II on the basis of biopharmaceutical classification system (BCS), what indicates that it has low solubility in aqueous solvents. The use of API salts has attracted attention due to their improvements in solubility, tolerability, higher rate and extent of absorption, and faster onset of the therapeutic effect. In this work, a new crystalline CIP monohydrated calcium salt (Ca(CIP)2.H2O) was successfully obtained and its crystal structure determined by single crystal X-ray diffraction analysis (SCXRD). Additionally, Ca(CIP)2.H2O was widely characterized by powder X-ray diffraction (PXRD), Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and submitted to solubility, intrinsic dissolution and accelerated stability studies. Ca(CIP)2.H2O exhibited higher solubility and dissolution rate than CIP-free form and was stable up to 6 months at 40°C (75%RH). Therefore, Ca(CIP)2.H2O may be a viable alternative for use in solid dosage forms.

Microfluidic systems for modeling digestive cancer: a review of recent progress

Purpose. This review aims to highlight current improvements in microfluidic devices designed for digestive cancer simulation. The review emphasizes the use of multicellular 3D tissue engineering models to understand the complicated biology of the tumor microenvironment (TME) and cancer progression. The purpose is to develop oncology research and improve digestive cancer patients' lives.Methods. This review analyzes recent research on microfluidic devices for mimicking digestive cancer. It uses tissue-engineered microfluidic devices, notably organs on a chip (OOC), to simulate human organ function in the lab. Cell cultivation on modern three-dimensional hydrogel platforms allows precise geometry, biological components, and physiological qualities. The review analyzes novel methodologies, key findings, and technical progress to explain this field's advances.Results. This study discusses current advances in microfluidic devices for mimicking digestive cancer. Micro physiological systems with multicellular 3D tissue engineering models are emphasized. These systems capture complex biochemical gradients, niche variables, and dynamic cell-cell interactions in the tumor microenvironment (TME). These models reveal stomach cancer biology and progression by duplicating the TME. Recent discoveries and technology advances have improved our understanding of gut cancer biology, as shown in the review.Conclusion. Microfluidic systems play a crucial role in modeling digestive cancer and furthering oncology research. These platforms could transform drug development and treatment by revealing the complex biology of the tumor microenvironment and cancer progression. The review provides a complete summary of recent advances and suggests future research for field professionals. The review's major goal is to further medical research and improve digestive cancer patients' lives.

Advancing drug discovery with deep attention neural networks

In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transformers, bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPTs) and bidirectional and auto-regressive transformers (BART). Delving into their core principles and multifaceted applications, we uncover their pivotal roles in catalyzing de novo drug design, predicting intricate molecular properties and deciphering elusive drug-target interactions. Despite challenges, these attention-based architectures hold unparalleled promise to drive transformative breakthroughs and accelerate progress in pharmaceutical research.

Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning

Unexpected metabolism could lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Thus, it is critical to predict and study the dominant routes of metabolism in the early stages of research.We describe the development and validation of a 'WhichEnzyme' model that accurately predicts the enzyme families most likely to be responsible for a drug-like molecule's metabolism. Furthermore, we combine this model with our previously published regioselectivity models for Cytochromes P450, Aldehyde Oxidases, Flavin-containing Monooxygenases, UDP-glucuronosyltransferases and Sulfotransferases - the most important Phase I and Phase II drug metabolising enzymes - and a 'WhichP450' model that predicts the Cytochrome P450 isoform(s) responsible for a compound's metabolism.The regioselectivity models are based on a mechanistic understanding of these enzymes' actions and use quantum mechanical simulations with machine learning methods to accurately predict sites of metabolism and the resulting metabolites. We train heuristics based on the outputs of the 'WhichEnzyme', 'WhichP450', and regioselectivity models to determine the most likely routes of metabolism and metabolites to be observed experimentally.Finally, we demonstrate that this combination delivers high sensitivity in identifying experimentally reported metabolites and higher precision than other methods for predicting in vivo metabolite profiles.

The combination therapy using tyrosine kinase receptors inhibitors and repurposed drugs to target patient-derived glioblastoma stem cells

The lesson from many studies investigating the efficacy of targeted therapy in glioblastoma (GBM) showed that a future perspective should be focused on combining multiple target treatments. Our research aimed to assess the efficacy of drug combinations against glioblastoma stem cells (GSCs). Patient-derived cells U3042, U3009, and U3039 were obtained from the Human Glioblastoma Cell Culture resource. Additionally, the study was conducted on a GBM commercial U251 cell line. Gene expression analysis related to receptor tyrosine kinases (RTKs), stem cell markers and genes associated with significant molecular targets was performed, and selected proteins encoded by these genes were assessed using the immunofluorescence and flow cytometry methods. The cytotoxicity studies were preceded by analyzing the expression of specific proteins that serve as targets for selected drugs. The cytotoxicity study using the MTS assay was conducted to evaluate the effects of selected drugs/candidates in monotherapy and combinations. The most cytotoxic compounds for U3042 cells were Disulfiram combined with Copper gluconate (DSF/Cu), Dacomitinib, and Foretinib with IC50 values of 52.37 nM, 4.38 µM, and 4.54 µM after 24 h incubation, respectively. Interactions were assessed using SynergyFinder Plus software. The analysis enabled the identification of the most effective drug combinations against patient-derived GSCs. Our findings indicate that the most promising drug combinations are Dacomitinib and Foretinib, Dacomitinib and DSF/Cu, and Foretinib and AZD3759. Since most tested combinations have not been previously examined against glioblastoma stem-like cells, these results can shed new light on designing the therapeutic approach to target the GSC population.

Best practices for machine learning in antibody discovery and development

In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.

Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates

Background

Current effective breast cancer treatment options have severe side effects, highlighting a need for new therapies. Drug repurposing can accelerate improvements to care, as FDA-approved drugs have known safety and pharmacological profiles. Some drugs for other conditions, such as metformin, an antidiabetic, have been tested in clinical trials for repurposing for breast cancer. Here, we exploit the genetics of breast cancer and linked predisposing diseases to propose novel drug repurposing. We hypothesize that if a predisposing disease contributes to breast cancer pathology, identifying the pleiotropic genes related to the risk of cancer could prioritize drug targets, among all drugs treating a predisposing disease. We aim to develop a method to not only prioritize drug repurposing, but also to highlight shared etiology explaining repurposing.

Methods

We compile breast cancer's predisposing diseases from literature. For each predisposing disease, we use GWAS summary statistics to identify genes in loci showing genetic correlation with breast cancer. Then, we use a network approach to link these shared genes to canonical pathways, and similarly for all drugs treating the predisposing disease, we link their targets to pathways. In this manner, we are able to prioritize a list of drugs based on each predisposing disease, with each drug linked to a set of implicating pathways. Finally, we evaluate our recommendations against drugs currently under investigation for breast cancer.

Results

We identify 84 loci harboring mutations with positively correlated effects between breast cancer and its predisposing diseases; these contain 194 identified shared genes. Out of the 112 drugs indicated for the predisposing diseases, 76 drugs can be linked to shared genes via pathways (candidate drugs for repurposing). Fifteen out of these candidate drugs are already in advanced clinical trial phases or approved for breast cancer (OR = 9.28, p = 7.99e-03, one-sided Fisher's exact test), highlighting the ability of our approach to identify likely successful candidate drugs for repurposing.

Conclusions

Our novel approach accelerates drug repurposing for breast cancer by leveraging shared genetics with its known risk factors. The result provides 59 novel candidate drugs alongside biological insights supporting each recommendation.

Adapt-cMolGPT: A Conditional Generative Pre-Trained Transformer with Adapter-Based Fine-Tuning for Target-Specific Molecular Generation

Small-molecule drug design aims to generate compounds that target specific proteins, playing a crucial role in the early stages of drug discovery. Recently, research has emerged that utilizes the GPT model, which has achieved significant success in various fields to generate molecular compounds. However, due to the persistent challenge of small datasets in the pharmaceutical field, there has been some degradation in the performance of generating target-specific compounds. To address this issue, we propose an enhanced target-specific drug generation model, Adapt-cMolGPT, which modifies molecular representation and optimizes the fine-tuning process. In particular, we introduce a new fine-tuning method that incorporates an adapter module into a pre-trained base model and alternates weight updates by sections. We evaluated the proposed model through multiple experiments and demonstrated performance improvements compared to previous models. In the experimental results, Adapt-cMolGPT generated a greater number of novel and valid compounds compared to other models, with these generated compounds exhibiting properties similar to those of real molecular data. These results indicate that our proposed method is highly effective in designing drugs targeting specific proteins.

(3D) Bioprinting-Next Dimension of the Pharmaceutical Sector

To create a review of the published scientific literature on the benefits and potential perspectives of the use of 3D bio-nitrification in the field of pharmaceutics. This work was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting meta-analyses and systematic reviews. The scientific databases PubMed, Scopus, Google Scholar, and ScienceDirect were used to search and extract data using the following keywords: 3D bioprinting, drug research and development, personalized medicine, pharmaceutical companies, clinical trials, drug testing. The data points to several aspects of the application of bioprinting in pharmaceutics were reviewed. The main applications of bioprinting are in the development of new drug molecules as well as in the preparation of personalized drugs, but the greatest benefits are in terms of drug screening and testing. Growth in the field of 3D printing has facilitated pharmaceutical applications, enabling the development of personalized drug screening and drug delivery systems for individual patients. Bioprinting presents the opportunity to print drugs on demand according to the individual needs of the patient, making the shape, structure, and dosage suitable for each of the patient's physical conditions, i.e., print specific drugs for controlled release rates; print porous tablets to reduce swallowing difficulties; make transdermal microneedle patches to reduce patient pain; and so on. On the other hand, bioprinting can precisely control the distribution of cells and biomaterials to build organoids, or an Organ-on-a-Chip, for the testing of drugs on printed organs mimicking specified disease characteristics instead of animal testing and clinical trials. The development of bioprinting has the potential to offer customized drug screening platforms and drug delivery systems meeting a range of individualized needs, as well as prospects at different stages of drug development and patient therapy. The role of bioprinting in preclinical and clinical testing of drugs is also of significant importance in terms of shortening the time to launch a medicinal product on the market.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

Envisioning how to advance the MASH field

Since 1980, the cumulative effort of scientists and health-care stakeholders has advanced the prerequisites to address metabolic dysfunction-associated steatotic liver disease (MASLD), a prevalent chronic non-communicable liver disease. This effort has led to, among others, the approval of the first drug specific for metabolic dysfunction-associated steatohepatitis (MASH; formerly known as nonalcoholic steatohepatitis). Despite substantial progress, MASLD is still a leading cause of advanced chronic liver disease, including primary liver cancer. This Perspective contextualizes the nomenclature change from nonalcoholic fatty liver disease to MASLD and proposes important considerations to accelerate further progress in the field, optimize patient-centric multidisciplinary care pathways, advance pharmacological, behavioural and diagnostic research, and address health disparities. Key regulatory and other steps necessary to optimize the approval and access to upcoming additional pharmacological therapeutic agents for MASH are also outlined. We conclude by calling for increased education and awareness, enhanced health system preparedness, and concerted action by policy-makers to further the public health and policy agenda to achieve at least parity with other non-communicable diseases and to aid in growing the community of practice to reduce the human and economic burden and end the public health threat of MASLD and MASH by 2030.

Costs of Drug Development and Research and Development Intensity in the US, 2000-2018

Importance

Understanding the cost of drug development can help inform the development of policies to reduce costs, encourage innovation, and improve patient access to drugs.

Objective

To estimate the cost of drug development by therapeutic class and trends in pharmaceutical research and development (R&D) intensity over time.

Design, Setting, and Participants

In this economic evaluation study, an analytical model of drug development constructed using public and proprietary sources that collectively cover data from 2000 to 2018 was used to estimate the cost of bringing a drug to market, overall and for specific therapeutic classes. The analysis for the study was completed in October 2020.

Main Outcomes and Measures

Three measures of development cost from nonclinical through postmarketing stages were estimated: mean out-of-pocket cost or cash outlay, mean expected cost, and mean expected capitalized cost. Pharmaceutical R&D intensity, defined as the ratio of R&D spending to total sales, from 2008 to 2019, based on the time frame for available data, was also analyzed.

Results

The estimated mean cost of developing a new drug was approximately $172.7 million (2018 dollars) (range, $72.5 million for genitourinary to $297.2 million for pain and anesthesia), inclusive of postmarketing studies. The cost increased to $515.8 million when cost of failures was included. When the costs of failures and capital were included, the mean expected capitalized cost of drug development increased to $879.3 million (range, $378.7 million for anti-infectives to $1756.2 million for pain and anesthesia); results varied widely by therapeutic class. The pharmaceutical industry as a whole experienced a decline of 15.6% in sales but increased R&D intensity from 11.9% to 17.7% from 2008 to 2019. By contrast, R&D intensity of large pharmaceutical companies increased from 16.6% to 19.3%, whereas sales increased by 10.0% (from $380.0 to $418.0 billion) over the same 2008 to 2019 period, even though the cost of drug development remained relatively stable or may have even decreased.

Conclusions and Relevance

In this economic evaluation of new drug development costs, even though the cost of drug development appears to have remained stable, R&D intensity of large pharmaceutical companies remained relatively unchanged, despite substantial growth in revenues during this period. These findings can inform the design of drug-related policies and their potential impacts on innovation and competition.

Innovative therapeutic strategies to overcome radioresistance in breast cancer

Despite a comparatively favorable prognosis relative to other malignancies, breast cancer continues to significantly impact women's health globally, partly due to its high incidence rate. A critical factor in treatment failure is radiation resistance - the capacity of tumor cells to withstand high doses of ionizing radiation. Advancements in understanding the cellular and molecular mechanisms underlying radioresistance, coupled with enhanced characterization of radioresistant cell clones, are paving the way for the development of novel treatment modalities that hold potential for future clinical application. In the context of combating radioresistance in breast cancer, potential targets of interest include long non-coding RNAs (lncRNAs), micro RNAs (miRNAs), and their associated signaling pathways, along with other signal transduction routes amenable to pharmacological intervention. Furthermore, technical, and methodological innovations, such as the integration of hyperthermia or nanoparticles with radiotherapy, have the potential to enhance treatment responses in patients with radioresistant breast cancer. This review endeavors to provide a comprehensive survey of the current scientific landscape, focusing on novel therapeutic advancements specifically addressing radioresistant breast cancer.

D3 Receptor-Targeted Cariprazine: Insights from Lab to Bedside

Until the late 1800s, drug development was a chance finding based on observations and repeated trials and errors. Today, drug development must go through many iterations and tests to ensure it is safe, potent, and effective. This process is a long and costly endeavor, with many pitfalls and hurdles. The aim of the present review article is to explore what is needed for a molecule to move from the researcher bench to the patients' bedside, presented from an industry perspective through the development program of cariprazine. Cariprazine is a relatively novel antipsychotic medication, approved for the treatment of schizophrenia, bipolar mania, bipolar depression, and major depression as an add-on. It is a D3-preferring D3-D2 partial agonist with the highest binding to the D3 receptors compared to all other antipsychotics. Based on the example of cariprazine, there are several key factors that are needed for a molecule to move from the researcher bench to the patients' bedside, such as targeting an unmet medical need, having a novel mechanism of action, and a smart implementation of development plans.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

BACKGROUND

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment.

METHODS

The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023.

RESULTS

Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion.

CONCLUSION

By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

Protein Structure Inspired Drug Discovery

Drug discovery starts with known function, either of a compound or a protein, in-turn prompting investigations to probe 3D structure of the compound-protein interface. As protein structure determines function, we hypothesized that unique 3D structural motifs represent primary information denoting unique function that can drive discovery of novel agents. Using a physics-based protein structure analysis platform developed by us, designed to conduct computationally intensive analysis at supercomputing speeds, we probed a high-resolution protein x-ray crystallographic library developed by us. We selected 3D structural motifs whose function was not otherwise established, that offered environments supporting binding of drug-like chemicals and were present on proteins that were not established therapeutic targets. For each of eight potential binding pockets on six different proteins we accessed a 60 million compound library and used our analysis platform to evaluate binding. Using eight-day colony formation assays acquired compounds were screened for efficacy against human breast, prostate, colon and lung cancer cells and toxicity against human bone marrow stem cells. Compounds selectively inhibiting cancer growth segregated to two pockets on separate proteins. The compound, Dxr2-017, exhibited selective activity against human melanoma cells in the NCI-60 cell line screen, had an IC50 of 19 nM against human melanoma M14 cells in our eight-day assay, while over 2100-fold higher concentrations inhibited stem cells by less than 30%. We show that Dxr2-017 induces anoikis, a unique form of programmed cell death in need of targeted therapeutics. The predicted target protein for Dxr2-017 is expressed in bacteria, not in humans. This supports our strategy of focusing on unique 3D structural motifs. It is known that functionally important 3D structures are evolutionarily conserved. Here we demonstrate proof-of-concept that protein structure represents high value primary data to support discovery of novel therapeutics. This approach is widely applicable.

Orthotopic and metastatic tumour models in preclinical cancer research

Mouse models of disease play a pivotal role at all stages of cancer drug development. Cell-line derived subcutaneous tumour models are predominant in early drug discovery, but there is growing recognition of the importance of the more complex orthotopic and metastatic tumour models for understanding both target biology in the correct tissue context, and the impact of the tumour microenvironment and the immune system in responses to treatment. The aim of this review is to highlight the value that orthotopic and metastatic models bring to the study of tumour biology and drug development while pointing out those models that are most likely to be encountered in the literature. Important developments in orthotopic models, such as the increasing use of early passage patient material (PDXs, organoids) and humanised mouse models are discussed, as these approaches have the potential to increase the predictive value of preclinical studies, and ultimately improve the success rate of anticancer drugs in clinical trials.

New Anticancer Drugs: Reliably Assessing "Value" While Addressing High Prices

Countries face challenges in paying for new drugs. High prices are driven in part by exploding drug development costs, which, in turn, are driven by essential but excessive regulation. Burdensome regulation also delays drug development, and this can translate into thousands of life-years lost. We need system-wide reform that will enable less expensive, faster drug development. The speed with which COVID-19 vaccines and AIDS therapies were developed indicates this is possible if governments prioritize it. Countries also differ in how they value drugs, and generally, those willing to pay more have better, faster access. Canada is used as an example to illustrate how "incremental cost-effectiveness ratios" (ICERs) based on measures such as gains in "quality-adjusted life-years" (QALYs) may be used to determine a drug's value but are often problematic, imprecise assessments. Generally, ICER/QALY estimates inadequately consider the impact of patient crossover or long post-progression survival, therapy benefits in distinct subpopulations, positive impacts of the therapy on other healthcare or societal costs, how much governments willingly might pay for other things, etc. Furthermore, a QALY value should be higher for a lethal or uncommon disease than for a common, nonlethal disease. Compared to international comparators, Canada is particularly ineffective in initiating public funding for essential new medications. Addressing these disparities demands urgent reform.

Identification of a novel DNA repair inhibitor using an in silico driven approach shows effective combinatorial activity with genotoxic agents against multidrug-resistant Escherichia coli

Increasing antimicrobial drug resistance represents a global existential threat. Infection is a particular problem in immunocompromised individuals, such as patients undergoing cancer chemotherapy, due to the targeting of rapidly dividing cells by antineoplastic agents. We recently developed a strategy that targets bacterial nucleotide excision DNA repair (NER) to identify compounds that act as antimicrobial sensitizers specific for patients undergoing cancer chemotherapy. Building on this, we performed a virtual drug screening of a ~120,000 compound library against the key NER protein UvrA. From this, numerous target compounds were identified and of those a candidate compound, Bemcentinib (R428), showed a strong affinity toward UvrA. This NER protein possesses four ATPase sites in its dimeric state, and we found that Bemcentinib could inhibit UvrA's ATPase activity by ~90% and also impair its ability to bind DNA. As a result, Bemcentinib strongly diminishes NER's ability to repair DNA in vitro. To provide a measure of in vivo activity we discovered that the growth of Escherichia coli MG1655 was significantly inhibited when Bemcentinib was combined with the DNA damaging agent 4-NQO, which is analogous to UV. Using the clinically relevant DNA-damaging antineoplastic cisplatin in combination with Bemcentinib against the urological sepsis-causing E. coli strain EC958 caused complete growth inhibition. This study offers a novel approach for the potential development of new compounds for use as adjuvants in antineoplastic therapy.

A probabilistic knowledge graph for target identification

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.

The increasing costs of medicines and their implications for patients, physicians and the health system

Most new medicines entering the market are high-cost speciality drugs. These drugs can cost tens to hundreds of thousands of dollars per course of treatment and in some cases millions of dollars per dose. Approximately half of all spending on medicines is projected to target only 2-3% of patients, raising important questions about resource allocation. While there is no doubt that breakthrough innovations have transformed clinical care in some disciplines, it is also true that cost is becoming one of the primary barriers to treatment access and that many new medicines do not provide value commensurate with their prices. This article examines pricing trends, the reasons for high prices and their implications for access and clinical practice.

ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery

Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement the HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose; however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches that combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking. In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening while also being designed for use in high-performance computing (HPC) environments.

Decentralisation in Clinical Trials and Patient Centricity: Benefits and Challenges

Decentralised clinical trials (DCTs) encompass various terms such as virtual, home-based, remote and siteless trials. The objectives of DCTs are to enhance the ease of participation for patients in clinical trials by minimising or removing the necessity for trial subjects to travel to the trial sites. This approach has been shown to reduce drop-out rates, increase study effectiveness and ultimately get life-altering drugs to market faster-saving sponsors billions. At the outset, DCTs deploy a wide range of digital technologies to collect safety and efficacy data from study participants, providing study treatments and performing investigations from the comfort of the patient's own home. The aim of decentralised trials includes patient centricity, enhanced efficacy in clinical trial conduct and generating real-world data. This is done by not only making it convenient for the patient to participate in the trial execution, but also involving them from the planning stage and taking their inputs during designing of trials and consenting documentation, understanding their treatment requirements and designing the studies accordingly. Various regulatory authorities have published guidelines governing DCT principles, especially after the coronavirus disease 2019 (COVID-19) experience of undertaking multicentric clinical trials. Both United States Food and Drug Administration (USFDA) and European Medicines Agency (EMA) have newer, recently updated guidelines to capture this growing reality to undertake clinical trials using patient technology or patient-centric technologies. Other regulatory agencies are accepting data generated using decentralised and patient-centric technologies and making an effort to include elements of decentralised trials in their regulatory guidelines. Decentralised trials follow a hybrid approach to have a balanced mix of remote and in-person data collection and trial procedures. Decentralised and patient-centric approaches are the future of any organisation for the conduct of clinical trials. Globally, all sponsor pharmaceutical companies must start undertaking drug development and clinical trials using a decentralised approach while keeping patient centricity in mind.

A Framework for the Fair Pricing of Medicines

As high-cost medicines put increasing pressure on public health care budgets, the need to identify 'fair' prices for medicines has never been greater. This paper proposes a framework, built upon fundamental economic principles, that allows for the consideration of 'fair' prices for medicines. The framework incorporates key considerations from conventional supply-side and demand-side approaches for specifying a cost-effectiveness 'threshold', including the health opportunity cost borne by other patients ([Formula: see text]) and society's willingness to pay for marginal improvements in population health ([Formula: see text]). The costs incurred by manufacturers in developing and supplying new medicines are also considered, as are the incentives for manufacturers to strategically price up to any common price per unit of benefit (cost-effectiveness 'threshold') specified by the payer. The framework finds that, at any 'fair' price, a medicine's dynamically calculated incremental cost-effectiveness ratio (ICER) lies below [Formula: see text]. When pricing medicines collectively, the framework finds that a common price below [Formula: see text] is required to maximize population health (consumer surplus) or to maximize total welfare (consumer and producer surplus). This framework has important policy implications for payers who wish to improve population health outcomes from constrained health care budgets. In particular, existing approaches to 'value-based pricing' should be reconsidered to ensure that patients receive a 'fair' share of the resulting economic surplus.

Probability of Regulatory Approval Over Time: A Cohort Study of Cancer Therapies

PURPOSE

New cancer therapies are frequently evaluated in multiple disease indications. We evaluated whether the probability of achieving US Food and Drug Administration (FDA) approval for a new cancer therapy changes with time.

METHODS

We identified a cohort of anticancer drugs with a first registered efficacy trial from 2007 to 2011 on ClinicalTrials.gov. We downloaded all clinical trials for each included drug from the initiation of efficacy testing to January 11, 2021. Each trial was categorized by cancer indication and assigned to investigational trajectories on the basis of unique drug-indication pairings. We performed a univariate Cox's proportional hazards regression to assess the probability of a trajectory leading to regulatory approval over time since initiation of the first efficacy trial for a given drug.

RESULTS

We included 213 drugs in our cohort, of which 37 (17.4%) received FDA approval in at least one oncology indication. In our primary analysis, we found a 15% decrease in the probability of approval for every year since initiation of the first efficacy trial (hazard ratio [HR], 0.85 [95% CI, 0.73 to 0.99]; P = .032). We found a 45% increase in the probability of approval for the first trajectory launched for a given drug in comparison with all others (HR, 0.55 [95% CI, 0.33 to 0.91]; P = .021).

CONCLUSION

Drug-indication pairings pursued years after initial testing for efficacy have lowered probability of affecting care. Clinical trial investigators, sponsors, and regulatory bodies may benefit from awareness of this trend when considering both early and late trajectory trials in a drug's development.

Importance of aligning the implementation of new payment models for innovative pharmaceuticals in European countries

INTRODUCTION

The uptake of complex technologies and platforms has resulted in several challenges in the pricing and reimbursement of innovative pharmaceuticals. To address these challenges, plenty of concepts have already been described in the scientific literature about innovative value judgment or payment models, which are either (1) remaining theoretical; or (2) applied only in pilots with limited impact on patient access; or (3) applied so heterogeneously in many different countries that it prevents the health care industry from meeting expectations of HTA bodies and health care payers in the evidence requirements or offerings in different jurisdictions.

AREAS COVERED

This paper provides perspectives on how to reduce the heterogeneity of pharmaceutical payment models across European countries in five areas, including 1) extended evaluation frameworks, 2) performance-based risk-sharing agreements, 3) pooled procurement for low volume or urgent technologies, 4) alternative access schemes, and 5) delayed payment models for technologies with high upfront costs.

EXPERT OPINION

Whilst pricing and reimbursement decisions will remain a competence of EU member states, there is a need for alignment of European pharmaceutical payment model components in critical areas with the ultimate objective of improving the equitable access of European patients to increasingly complex pharmaceutical technologies.

Computational drug development for membrane protein targets

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Conformational diversity and protein-protein interfaces in drug repurposing in Ras signaling pathway

We focus on drug repurposing in the Ras signaling pathway, considering structural similarities of protein-protein interfaces. The interfaces formed by physically interacting proteins are found from PDB if available and via PRISM (PRotein Interaction by Structural Matching) otherwise. The structural coverage of these interactions has been increased from 21 to 92% using PRISM. Multiple conformations of each protein are used to include protein dynamics and diversity. Next, we find FDA-approved drugs bound to structurally similar protein-protein interfaces. The results suggest that HIV protease inhibitors tipranavir, indinavir, and saquinavir may bind to EGFR and ERBB3/HER3 interface. Tipranavir and indinavir may also bind to EGFR and ERBB2/HER2 interface. Additionally, a drug used in Alzheimer's disease can bind to RAF1 and BRAF interface. Hence, we propose a methodology to find drugs to be potentially used for cancer using a dataset of structurally similar protein-protein interface clusters rather than pockets in a systematic way.

DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles

Evaluation of chemical drug-likeness is essential for the discovery of high-quality drug candidates while avoiding unwarranted biological and clinical trial costs. A high-quality drug candidate should have promising drug-like properties, including pharmacological activity, suitable physicochemical and ADMET properties. Hence, in silico prediction of chemical drug-likeness has been proposed while being a challenging task. Although several prediction models have been developed to assess chemical drug-likeness, they have such drawbacks as sample dependence and poor interpretability. In this study, we developed a novel strategy, named DBPP-Predictor, to predict chemical drug-likeness based on property profile representation by integrating physicochemical and ADMET properties. The results demonstrated that DBPP-Predictor exhibited considerable generalization capability with AUC (area under the curve) values from 0.817 to 0.913 on external validation sets. In terms of application feasibility analysis, the results indicated that DBPP-Predictor not only demonstrated consistent and reasonable scoring performance on different data sets, but also was able to guide structural optimization. Moreover, it offered a new drug-likeness assessment perspective, without significant linear correlation with existing methods. We also developed a free standalone software for users to make drug-likeness prediction and property profile visualization for their compounds of interest. In summary, our DBPP-Predictor provided a valuable tool for the prediction of chemical drug-likeness, helping to identify appropriate drug candidates for further development.

Cost of Exempting Sole Orphan Drugs From Medicare Negotiation

Importance

The Inflation Reduction Act (IRA) requires Medicare to negotiate prices for some high-spending drugs but exempts drugs approved solely for the treatment of a single rare disease.

Objective

To estimate Medicare spending and global revenues for drugs that might have been exempt from negotiation from 2012 to 2021.

Design, Setting, and Participants

This cross-sectional study analyzed drugs that met the IRA threshold for price negotiation (Medicare spending >$200 million/y) in any year from 2012 to 2021 and had an Orphan Drug Act designation. We stratified drugs into 4 mutually exclusive categories: approved for a single rare disease (sole orphan), approved for multiple rare diseases (multiorphan), initially approved for a rare disease and subsequently approved for a nonrare disease (orphan first), and initially approved for a nonrare disease and subsequently approved for a rare disease (non-orphan first).

Outcomes

The primary outcomes were the number of sole orphan drugs, estimated Medicare spending on those drugs from 2012 to 2021, and global revenue since launch.

Results

Among 282 drugs, 95 (34%) were approved to treat at least 1 rare disease, including 25 sole orphan drugs (26%), 20 multiorphan drugs (21%), 13 orphan first drugs (14%), and 37 non-orphan first drugs (39%). From 2012 to 2021, Medicare spending on sole orphan drugs increased from $3.4 billion to $10.0 billion. Each year, a median (IQR) of $2.5 ($1.9-$2.6) billion in Medicare spending would have been excluded from price negotiation because of the sole orphan exemption. The cumulative global revenue of the median (IQR) sole orphan drug was $11 ($6.6-$19.2) billion.

Conclusions and Relevance

The sole orphan exemption will exclude billions of dollars of Medicare drug spending from price negotiation. The high level of global revenues achieved by these drugs, however, suggests that special exemption is unnecessary for them to achieve financial success. Congress could consider removing the sole orphan exemption to obtain additional savings for patients and taxpayers and to eliminate any potential disincentive for developing additional indications for these drugs.

Valutazione dell’innovatività e negoziazione di prezzi e rimborso dei farmaci: raccomandazioni da un panel di esperti

This paper illustrates the recommendations of a Working Group (WG) on the assessment of drugs innovativeness and the negotiation of price and reimbursement. The WG included researchers, institutions, clinicians, patient representatives and pharmaceutical companies.

The first part of the contribution summarizes the literature on drug pricing models, which was considered in the WG, and, in particular, the pricing criteria, the evaluation and negotiation processes, the management of the uncertainty of the evidence, the use of cross-reference pricing and price negotiation for new indications of existing drugs.

The second part illustrates the results of the WG with a focus on innovativeness assessment, value framework and price negotiation. The main recommendations of the WG are: to define more specific criteria for the identification of comparators and endpoints for macro therapeutic areas/settings; to produce guidelines on the use of indirect comparisons and studies supporting this evidence; to consider the drug value as the main driver of price and reimbursement negotiation; to maintain flexibility in the negotiation process, but, at the same time, to give greater structure and predictability in the assessment of value for money, with a more qualified role of cost-effectiveness and a range of threshold values for the incremental cost-effectiveness ratio; to selectively reintroduce Managed Entry Agreements and the Indication-based pricing model; to implement an early dialogue between the Italian Medicine Agency and the pharmaceutical companies in order to optimize the negotiation process, and a structured involvement of scientific societies and patient representatives.

Investigating the Cell Entry Mechanism, Disassembly, and Toxicity of the Nanocage PCC-1: Insights into Its Potential as a Drug Delivery Vehicle

The porous coordination cage PCC-1 represents a new platform potentially useful for the cellular delivery of drugs with poor cell permeability and solubility. PCC-1 is a metal-organic polyhedron constructed from zinc metal ions and organic ligands through coordination bonds. PCC-1 possesses an internal cavity that is suitable for drug encapsulation. To better understand the biocompatibility of PCC-1 with human cells, the cell entry mechanism, disassembly, and toxicity of the nanocage were investigated. PCC-1 localizes in the nuclei and cytoplasm within minutes upon incubation with cells, independent of endocytosis and cargo, suggesting direct plasma membrane translocation of the nanocage carrying its guest in its internal cavity. Furthermore, the rates of cell entry correlate to extracellular concentrations, indicating that PCC-1 is likely diffusing passively through the membrane despite its relatively large size. Once inside cells, PCC-1 disintegrates into zinc metal ions and ligands over a period of several hours, each component being cleared from cells within 1 day. PCC-1 is relatively safe for cells at low micromolar concentrations but becomes inhibitory to cell proliferation and toxic above a concentration or incubation time threshold. However, cells surviving these conditions can return to homeostasis 3-5 days after exposure. Overall, these findings demonstrate that PCC-1 enters live cells by crossing biological membranes spontaneously. This should prove useful to deliver drugs that lack this capacity on their own, provided that the dosage and exposure time are controlled to avoid toxicity.

Cancer Drug Price and Novelty in Mechanism of Action

Importance

Many economic theories point to regulatory issues and subsidization of research and development costs as the primary factor in the high cancer drug prices in the US. Even so, the association between the median annual cost and novelty of cancer drugs approved in the US remains unclear.

Objective

To evaluate the association between the median annual cost and novelty of cancer drugs approved in the US over a 6-year period.

Design, Setting, and Participants

This cross-sectional study included all cancer drugs approved by the US Food and Drug Administration (FDA) from January 1, 2015, to December 31, 2020. Drug names, indications, manufacturer, dosage, and measures of activity/efficacy were extracted from the FDA announcement. The search was performed in December 2021. Data were analyzed from January 2022 until April 2022.

Main Outcomes and Measures

Annual cost of treatment was calculated based on average wholesale price collected from the 2021 Micromedex Red Book database. Mechanism of action was inferred from trial publication or its references.

Results

There were 224 cancer drug approvals across 119 individual drugs, with a median annual cost of $196 000 (IQR, $170 000-$277 000). Gene and viral therapies were the most expensive (median, $448 000 [IQR, $448 000-$479 000]), followed by small molecule therapy (median, $244 000 [IQR, $203 000-$321 000), and biologics (median, $185 000 [IQR, $148 000-$195 000]). There was no significant difference in cost between first-in-class, next-in-class, and subsequent approvals of an already approved drug.

Conclusions and Relevance

Findings of this study indicate that the median annual price of anticancer drugs in the US is not associated with the novelty of their mechanism of action.

Organ-on-a-chip models for development of cancer immunotherapies

Cancer immunotherapy has emerged as a promising approach in the treatment of diverse cancer types. However, the development of novel immunotherapeutic agents faces persistent challenges due to poor translation from preclinical to clinical stages. To address these challenges, the integration of microfluidic models in research efforts has recently gained traction, bridging the gap between in vitro and in vivo systems. This approach enables modeling of the complex human tumor microenvironment and interrogation of cancer-immune interactions. In this review, we analyze the current and potential applications of microfluidic tumor models in cancer immunotherapy development. We will first highlight current trends in the immunooncology landscape. Subsequently, we will discuss recent examples of microfluidic models applied to investigate mechanisms of immune-cancer interactions and for developing and screening cancer immunotherapies in vitro. First steps toward their validation for predicting human in vivo outcomes are discussed. Finally, promising opportunities that microfluidic tumor models offer are highlighted considering their advantages and current limitations, and we suggest possible next steps toward their implementation and integration into the immunooncology drug development process.

Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro

Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.

Magistral Phage Preparations: Is This the Model for Everyone?

Phage therapy is increasingly put forward as a promising additional tool to help curb the global antimicrobial resistance crisis. However, industrially manufactured phage medicinal products are currently not available on the European Union and United States markets. In addition, it is expected that the business purpose-driven phage products that are supposed to be marketed in the future would mainly target commercially viable bacterial species and clinical indications, using fixed phage cocktails. hospitals or phage therapy centers aiming to help all patients with difficult-to-treat infections urgently need adequate phage preparations. We believe that national solutions based on the magistral preparation of personalized (preadapted) phage products by hospital and academic facilities could bring an immediate solution and could complement future industrially manufactured products. Moreover, these unlicensed phage preparations are presumed to be more efficient and to elicit less bacterial phage resistance issues than fixed phage cocktails, claims that need to be scientifically substantiated as soon as possible. Just like Belgium, other (European) countries could develop a magistral phage preparation framework that would exist next to the conventional medicinal product development and licensing pathways. However, it is important that the current producers of personalized phage products are provided with pragmatic quality and safety assurance requirements, which are preferably standardized (at least at the European level), and are tiered based on benefit-risk assessments at the individual patient level. Pro bono phage therapy providers should be supported and not stopped by the imposition of industry standards such as Good Manufacturing Practice requirements. Keywords: antimicrobial resistance; antibiotic resistance; bacterial infection; bacteriophage therapy; magistral preparation.

India's Decision to Deny an Extension of Patent for Bedaquiline: A Public Health Imperative

Tuberculosis (TB) remains a major global public health concern, which has become worse in low- and middle-income nations due to the rise of drug-resistant strains of the disease. Bedaquiline, a groundbreaking anti-TB drug, shows great promise for addressing multidrug-resistant TB (MDR-TB). However, the recent decision by India to reject the application for a patent extension on bedaquiline raises crucial questions regarding access to essential medicines and public health requirements. This article examines the significance of India's rejection of the patent extension for bedaquiline, outlining the global implications of this decision and its impact on intellectual property rights, access to medicines, and the future of drug development. India's stance serves as a model for other countries to prioritize public health in their patent-related decisions, underlining the need for a balanced approach to intellectual property rights, innovation, and affordability in the pharmaceutical sector. In the context of the ongoing battle against drug-resistant TB, India's decision on bedaquiline signifies the evolving landscape of pharmaceutical patent practices in the service of global public health.

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

Analysis of pharma R&D productivity - a new perspective needed

R&D productivity continues to be the industry's grand challenge. We analyzed the R&D input, output, and outcome of 16 leading research-based pharmaceutical companies over 20 years (2001-2020). Our analysis shows that pharma companies increased their R&D spending at a compound annual growth rate of 6% (2001-2020) to an average R&D expenditure per company of $6.7 billion (2020). The companies in our investigation launched 251 new drugs representing 46% of all CDER-related FDA approvals in the past 20 years. The average R&D efficiency of big pharma was $6.16 billion total R&D expenditures per new drug. Almost half of the leading companies needed to compensate for their negative R&D productivity through mergers and acquisitions.

Functional network analysis identifies multiple virulence and antibiotic resistance targets in Stenotrophomonas maltophilia

Stenotrophomonas maltophilia, an emerging multidrug-resistant opportunistic bacterium in humans is of major concern for immunocompromised individuals for causing pneumonia and bloodborne infections. This bacterial pathogen is associated with a considerable fatality/case ratio, with up to 100%, when presented as hemorrhagic fever. It is resistant to commonly used drugs as well as to antibiotic combinations. In-silico based functional network analysis is a key approach to get novel insights into virulence and resistance in pathogenic organisms. This study included the protein-protein interaction (PPI) network analysis of 150 specific genes identified for antibiotic resistance mechanism and virulence pathways. Eight proteins, namely, PilL, FliA, Smlt2260, Smlt2267, CheW, Smlt2318, CheZ, and FliM were identified as hub proteins. Further docking studies of 58 selected phytochemicals were performed against the identified hub proteins. Deoxytubulosine and corosolic acid were found to be potent inhibitors of hub proteins of pathogenic S. maltophilia based on protein-ligand interactive study. Further pharmacophore studies are warranted with these molecules to develop them as novel antibiotics against S. maltophilia.

Utilization of 3D bioprinting technology in creating human tissue and organoid models for preclinical drug research - State-of-the-art

Rapid development of tissue engineering in recent years has increased the importance of three-dimensional (3D) bioprinting technology as novel strategy for fabrication functional 3D tissue and organoid models for pharmaceutical research. 3D bioprinting technology gives hope for eliminating many problems associated with traditional cell culture methods during drug screening. However, there is a still long way to wider clinical application of this technology due to the numerous difficulties associated with development of bioinks, advanced printers and in-depth understanding of human tissue architecture. In this review, the work associated with relatively well-known extrusion-based bioprinting (EBB), jetting-based bioprinting (JBB), and vat photopolymerization bioprinting (VPB) is presented and discussed with the latest advances and limitations in this field. Next we discuss state-of-the-art research of 3D bioprinted in vitro models including liver, kidney, lung, heart, intestines, eye, skin as well as neural and bone tissue that have potential applications in the development of new drugs.

Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Synergistic combination of carvedilol, amlodipine, amitriptyline, and antibiotics as an alternative treatment approach for the susceptible and multidrug-resistant A. baumannii infections via drug repurposing

We evaluated in vitro activity of 13 drugs used in the treatment of some non-communicable diseases via repurposing to determine their potential use in the treatment of Acinetobacter baumannii infections caused by susceptible and multidrug-resistant strains. A. baumannii is a multidrug-resistant Gram-negative bacteria causing nosocomial infections, especially in intensive care units. It has been identified in the WHO critical pathogen list and this emphasises urgent need for new treatment options. As the development of new therapeutics is expensive and time consuming, finding new uses of existing drugs via drug repositioning has been favoured. Antimicrobial susceptibility tests were conducted on all 13 drugs according to CLSI. Drugs with MIC values below 128 μg/mL and control antibiotics were further subjected to synergetic effect and bacterial time-kill analysis. Carvedilol-gentamicin (FICI 0.2813) and carvedilol-amlodipine (FICI 0.5625) were determined to have synergetic and additive effect, respectively, on the susceptible A. baumannii strain, and amlodipine-tetracycline (FICI 0.75) and amitriptyline-tetracycline (FICI 0.75) to have additive effect on the multidrug-resistant A. baumannii strain. Most remarkably, both amlodipine and amitriptyline reduced the MIC of multidrug-resistant, including some carbapenems, A. baumannii reference antibiotic tetracycline from 2 to 0.5 μg/mL, for 4-folds. All these results were further supported by bacterial time-kill assay and all combinations showed bactericidal activity, at certain hours, at 4XMIC. Combinations proposed in this study may provide treatment options for both susceptible and multidrug-resistant A. baumannii infections but requires further pharmacokinetics and pharmacodynamics analyses and in vivo re-evaluations using appropriate models.