Aaps J
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Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. ⋯ In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.
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Recent advances in our understanding of the intestinal stem cell niche and the role of key signaling pathways on cell growth and maintenance have allowed the development of fully differentiated epithelial cells in 3D organoids. Stem cell-derived organoids carry significant levels of proteins that are natively expressed in the gut and have important roles in drug transport and metabolism. They are, therefore, particularly relevant to study the gastrointestinal (GI) absorption of oral medications. ⋯ Importantly, because they are derived from individuals with different genotypes, environmental risk factors and drug sensitivity profiles, organoids are a highly relevant screening system for personalized therapy in both human and veterinary medicine. Lastly, and in the context of patient-specific congenital diseases, orthotopic transplantation of engineered organoids could repair and/or replace damaged epithelial tissues reported in various GI diseases, such as inflammatory bowel disease, cystic fibrosis, and tuft enteropathy. Ongoing translational research on organoids derived from dogs with naturally occurring digestive disorders has the potential to improve the predictability of preclinical models used for optimizing the therapeutic management of severe chronic enteropathies in human patients.
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Traumatic brain injury (TBI) is one of the leading causes of death and disability, particularly amongst the young and the elderly. The functions of the blood-brain barrier (BBB) and blood-cerebrospinal fluid barrier (BCSFB) are strongly impaired after TBI, thus affecting brain homeostasis. Following the primary mechanical injury that characterizes TBI, a secondary injury develops over time, including events such as edema formation, oxidative stress, neuroinflammation, and alterations in paracelullar and transcellular transport. ⋯ Importantly, BBB disregulation has been observed even years after TBI, concomitantly with neurological and psychosocial sequelae; however, treatments targeting the post-acute phase are scarce. Here, we review the mechanisms of primary and secondary injury of CNS barriers, the accumulating evidence showing long-term damage to these structures and some of the therapies that have targeted these mechanisms. Finally, we discuss how the injury characteristics (hemorrhagic vs non-hemorrhagic, involvement of head rotation, gray vs white matter), the sex, and the age of the patient need to be carefully considered to improve clinical trial design and outcome interpretation, and to improve future drug development.
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Despite the enormous research efforts that have been put into the development of central nervous system (CNS) drugs, the success rate in this area is still disappointing. To increase the successful rate in the clinical trials, first the problem of predicting human CNS drug distribution should be solved. As it is the unbound drug that equilibrates over membranes and is able to interact with targets, especially knowledge on unbound extracellular drug concentration-time profiles in different CNS compartments is important. ⋯ Furthermore, the currently available approaches on prediction of CNS pharmacokinetics are discussed, including in vitro, in vivo, ex vivo, and in silico approaches, with special focus on the powerful combination of in vivo microdialysis and PBPK modeling. Also, sources of variability on drug kinetics in the CNS are discussed. Finally, remaining gaps and challenges are highlighted and future directions are suggested.
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Meta Analysis
An Integrated Assessment of the Effects of Immunogenicity on the Pharmacokinetics, Safety, and Efficacy of Elotuzumab.
Elotuzumab is a humanized, immunostimulatory anti-signaling lymphocytic activation molecule F7 (SLAMF7) IgG1 monoclonal antibody indicated in combination with lenalidomide and dexamethasone for patients with multiple myeloma (MM) who have received 1-3 prior therapies. We assessed the immunogenicity of elotuzumab as a monotherapy and in combination with bortezomib/dexamethasone and lenalidomide/dexamethasone in patients with MM in five clinical studies, including the pivotal ELOQUENT-2 trial (NCT01239797). Anti-drug antibody (ADA) prevalence was determined using a validated bridging assay. ⋯ ADAs were associated with lower elotuzumab steady-state exposure; however, this result may have been confounded by differential myeloma protein levels. ADAs/NAbs were not associated with hypersensitivity, infusion reactions, or loss of elotuzumab efficacy. Using a novel visualization, we also demonstrate that there is no clear relationship between the occurrence and titer values of ADA/NAbs and progression-free survival and best overall response status in patients treated with elotuzumab.