One of the biggest challenges in pediatric drug development is defining a safe and effective dose in pediatric populations, which span across a wide age and development range from neonates to ...adolescents. Model-informed drug development approaches are particularly suited to address knowledge gaps including data leveraging to increase the success of pediatric studies. Considering the often limited number of patients available for study and logistic difficulties to collect the necessary data in pediatric populations, the application of pharmacometrics and modeling and simulation techniques can improve clinical trial efficiency, increase the probability of regulatory success, and optimize therapeutic individualization in support of dedicated trials. This review describes the state of pediatric model-informed drug development to define the right dose for children and provides suggestions for future development.
Only one class of targeted agents (anti-GD2 antibodies) has been incorporated into front-line therapy for neuroblastoma since the 1980s. The Neuroblastoma New Drug Development Strategy (NDDS) ...initiative commenced in 2012 to accelerate the development of new drugs for neuroblastoma. Advances have occurred, with eight of nine high-priority targets being evaluated in paediatric trials including anaplastic lymphoma kinase inhibitors being investigated in front-line, but significant challenges remain.
This article reports the conclusions of the second NDDS forum, which expanded across the Atlantic to further develop the initiative. Pre-clinical and clinical data for 40 genetic targets and mechanisms of action were prioritised and drugs were identified for early-phase trials.
Strategies to develop drugs targeting TERT, telomere maintenance, ATRX, alternative lengthening of telomeres (ALT), BRIP1 and RRM2 as well as direct targeting of MYCN are high priority and should be championed for drug discovery. Promising pre-clinical data suggest that targeting of ALT by ATM or PARP inhibition may be potential strategies. Drugs targeting CDK2/9, CDK7, ATR and telomere maintenance should enter paediatric clinical development rapidly. Optimising the response to anti-GD2 by combinations with chemotherapy, targeted agents and other immunological targets are crucial.
Delivering this strategy in the face of small patient cohorts, genomically defined subpopulations and a large number of permutations of combination trials, demands even greater international collaboration.
In conclusion, the NDDS provides an internationally agreed, biologically driven selection of prioritised genetic targets and drugs. Improvements in the strategy for conducting trials in neuroblastoma will accelerate bringing these new drugs more rapidly to front-line therapy.
•The Neuroblastoma New Drug Development Strategy initiative aims to accelerate new drugs development.•Twenty two of 40 targets were identified as high priority for neuroblastoma.•Targeting telomere maintenance, ATRX, BRIP1, RRM2 are high priority for drug discovery.•Drugs targeting CDK2/9, CDK7, ATR should enter paediatric clinical development rapidly.•Combining targeted therapies with immunological agents is crucial.
In the current context of the pandemic triggered by SARS-COV-2, the immunization of the population through vaccination is recognized as a public health priority. In the case of SARS‑COV‑2, the ...genetic sequencing was done quickly, in one month. Since then, worldwide research has focused on obtaining a vaccine. This has a major economic impact because new technological platforms and advanced genetic engineering procedures are required to obtain a COVID‑19 vaccine. The most difficult scientific challenge for this future vaccine obtained in the laboratory is the proof of clinical safety and efficacy. The biggest challenge of manufacturing is the construction and validation of production platforms capable of making the vaccine on a large scale.
Abstract
Despite the fact that deep learning has achieved remarkable success in various domains over the past decade, its application in molecular informatics and drug discovery is still limited. ...Recent advances in adapting deep architectures to structured data have opened a new paradigm for pharmaceutical research. In this survey, we provide a systematic review on the emerging field of graph convolutional networks and their applications in drug discovery and molecular informatics. Typically we are interested in why and how graph convolution networks can help in drug-related tasks. We elaborate the existing applications through four perspectives: molecular property and activity prediction, interaction prediction, synthesis prediction and de novo drug design. We briefly introduce the theoretical foundations behind graph convolutional networks and illustrate various architectures based on different formulations. Then we summarize the representative applications in drug-related problems. We also discuss the current challenges and future possibilities of applying graph convolutional networks to drug discovery.
Diabetes mellitus is a metabolic disorder that affects more than 460 million people worldwide. Type 1 diabetes (T1D) is caused by autoimmune destruction of β-cells, whereas type 2 diabetes (T2D) is ...caused by a hostile metabolic environment that leads to β-cell exhaustion and dysfunction. Currently, first-line medications treat the symptomatic insulin resistance and hyperglycaemia, but do not prevent the progressive decline of β-cell mass and function. Thus, advanced therapies need to be developed that either protect or regenerate endogenous β-cell mass early in disease progression or replace lost β-cells with stem cell-derived β-like cells or engineered islet-like clusters. In this Review, we discuss the state of the art of stem cell differentiation and islet engineering, reflect on current and future challenges in the area and highlight the potential for cell replacement therapies, disease modelling and drug development using these cells. These efforts in stem cell and regenerative medicine will lay the foundations for future biomedical breakthroughs and potentially curative treatments for diabetes.
The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for ...their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk‐informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk‐based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick‐start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.
Excitement about the dramatic increase in potential successful anticancer medicines in recent years is hampered by the high costs involved as well as the length of time traditional pathways take for ...regulatory approval. The translation of experimental clinical data into real-world evidence is also problematic. While the randomised controlled trial remains the gold standard for assessing efficacy and safety, there is increasing interest in the use of observational data to enable more rapid, informed and widespread availability and access to important anticancer medicines. Taking real-world evidence into account in regulatory and health technology assessment in a thoughtful and balanced fashion will enrich and justify sound decision-making.
•Adding real-world data to randomised controlled trials of anticancer drugs can increase external validity.•Many challenges remain before real-world evidence may become an integrated part of decision-making in drug development.•Even the use of advanced strategies to adjust for confounding factors may lead to biased efficacy estimates.•Use of RWD could reduce costs, minimise patient numbers in randomised trials and supplement or confirm results from RCTs.
The Mycobacterium abscessus complex is a group of emerging pathogens that are difficult to treat. There are no effective drugs for successful M. abscessus pulmonary infection therapy, and existing ...drug regimens recommended by the British or the American Thoracic Societies are associated with poor clinical outcomes. Therefore, novel antibacterial drugs are urgently needed to contain this global threat. The current anti‐M. abscessus small‐molecule drug development process can be enhanced by two parallel strategies—discovery of compounds from new chemical classes and commercial drug repurposing. This review focuses on recent advances in the finding of novel small‐molecule agents, and more particularly focuses on the activity, mode of action and structure–activity relationship of promising inhibitors from five different chemical classes—benzimidazoles, indole‐2‐carboxamides, benzothiazoles, 4‐piperidinoles, and oxazolidionones. We further discuss some other interesting small molecules, such as thiacetazone derivatives and benzoboroxoles, that are in the early stages of drug development, and summarize current knowledge about the efficacy of repurposable drugs, such as rifabutin, tedizolid, bedaquiline, and others. We finally review targets of therapeutic interest in M. abscessus that may be worthy of future drug and adjunct therapeutic development.
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