Taking phototherapeutics from concept to clinical launch.
Ontology highlight
ABSTRACT:
SUBMITTER: Vickerman BM
PROVIDER: S-EPMC8493544 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Light-activatable drugs offer the promise of controlled release with exquisite temporal and spatial resolution. However, light-sensitive prodrugs are typically converted to their active forms using short-wavelength irradiation, which displays poor tissue penetrance. We report herein erythrocyte-mediated assembly of long-wavelength-sensitive phototherapeutics. The activating wavelength of the constructs is readily preassigned by using fluorophores with the desired excitation wavelength ?(ex). Drug release from the erythrocyte carrier was confirmed by standard analytical tools and by the expected biological consequences of the liberated drugs in cell culture: methotrexate, binding to intracellular dihydrofolate reductase; colchicine, inhibition of microtubule polymerization; dexamethasone, induced nuclear migration of the glucocorticoid receptor.
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Project description:BackgroundRising cancer drug prices adversely affect patients' adherence and survival.ObjectiveWe aimed to identify and quantify factors associated with launch prices and post-launch price changes of injectable cancer drugs in the US from 2005 to 2023.Data and methodsAll anticancer drugs with US FDA approval between 2000 and 2022 were identified in the Drugs@FDA database. The sample was then restricted to cancer drugs covered under Medicare Part B (injectable drugs). Data characterizing each drug's clinical benefits, disease epidemiology, approved indications, competition, and price were obtained from FDA labels, the Global Burden of Disease study, and the Centers for Medicare and Medicaid Services. The association between launch/post-launch prices and collected variables was assessed in random-effects regressions.ResultsOf 170 cancer drugs with FDA approval between 2000 and 2022, we identified 66 (39%) injectable cancer drugs with quarterly price data from 2005 to 2023. In 2023, mean prices amounted to $27,688 per month, with an average price increase of 94% from 2005 to 2023. Launch and post-launch price changes were significantly associated with the treated disease epidemiology. A 1% decline in disease incidence was associated with a 0.2511% (p = 0.008) increase in launch prices and a 0.0086% (p = 0.032) annual increase in post-launch prices. Accordingly, launch prices were 120% (p = 0.051) higher for orphan than non-orphan drugs, with 3% (p = 0.008) greater annual post-launch price increases. Post-launch prices declined by up to -2% annually as new supplemental indications were approved for the same drug. We found no consistent association between launch/post-launch prices and the drugs' clinical benefit in terms of overall survival, progression-free survival, and tumor response. The market entry of new competitors was not associated with price reductions. 28 of 33 drug pairs within the same class had positive correlation coefficients. Pearson correlation coefficients were high (>0.80) for PD-1/PD-L1 inhibitors, CD38 antibodies, CD20 antibodies, HER2 antibodies, and mTOR inhibitors.ConclusionsCancer drug prices regularly increase faster than inflation; however, there is no evidence that launch prices and post-launch price changes are aligned with the clinical benefit a drug offers to patients. In particular, patients with rare diseases experience greater price increases for their orphan drugs. There is no evidence that brand-brand competition results in drug price reductions.
| S-EPMC10791980 | biostudies-literature
Project description:Herein, the use of red blood cells (RBCs) as carriers of cytoplasmically interned phototherapeutic agents is described. Photolysis promotes drug release from the RBC carrier thereby providing the means to target specific diseased sites. This strategy is realized with a vitamin B12-taxane conjugate (B12-TAX), in which the drug is linked to the vitamin via a photolabile CoC bond. The conjugate is introduced into mouse RBCs (mRBCs) via a pore-forming/pore-resealing procedure and is cytoplasmically retained due to the membrane impermeability of B12. Photolysis separates the taxane from the B12 cytoplasmic anchor, enabling the drug to exit the RBC carrier. A covalently appended Cy5 antenna sensitizes the conjugate (Cy5-B12-TAX) to far red light, thereby circumventing the intense light absorbing properties of hemoglobin (350-600 nm). Microscopy and imaging flow cytometry reveal that Cy5-B12-TAX-loaded mRBCs act as drug carriers. Furthermore, intravital imaging of mice furnish a real time assessment of circulating phototherapeutic-loaded mRBCs as well as evidence of the targeted photorelease of the taxane upon photolysis. Histopathology confirms that drug release occurs in a well resolved spatiotemporal fashion. Finally, acoustic angiography is employed to assess the consequences of taxane release at the tumor site in Nu/Nu-tumor-bearing mice.
| S-EPMC6739139 | biostudies-literature
Project description:ObjectiveThe goal of this study is to explore transformer-based models (eg, Bidirectional Encoder Representations from Transformers [BERT]) for clinical concept extraction and develop an open-source package with pretrained clinical models to facilitate concept extraction and other downstream natural language processing (NLP) tasks in the medical domain.MethodsWe systematically explored 4 widely used transformer-based architectures, including BERT, RoBERTa, ALBERT, and ELECTRA, for extracting various types of clinical concepts using 3 public datasets from the 2010 and 2012 i2b2 challenges and the 2018 n2c2 challenge. We examined general transformer models pretrained using general English corpora as well as clinical transformer models pretrained using a clinical corpus and compared them with a long short-term memory conditional random fields (LSTM-CRFs) mode as a baseline. Furthermore, we integrated the 4 clinical transformer-based models into an open-source package.Results and conclusionThe RoBERTa-MIMIC model achieved state-of-the-art performance on 3 public clinical concept extraction datasets with F1-scores of 0.8994, 0.8053, and 0.8907, respectively. Compared to the baseline LSTM-CRFs model, RoBERTa-MIMIC remarkably improved the F1-score by approximately 4% and 6% on the 2010 and 2012 i2b2 datasets. This study demonstrated the efficiency of transformer-based models for clinical concept extraction. Our methods and systems can be applied to other clinical tasks. The clinical transformer package with 4 pretrained clinical models is publicly available at https://github.com/uf-hobi-informatics-lab/ClinicalTransformerNER. We believe this package will improve current practice on clinical concept extraction and other tasks in the medical domain.
| S-EPMC7727351 | biostudies-literature
Project description:The availability of annotated corpora has facilitated the application of machine learning algorithms to concept extraction from clinical notes. However, high expenditure and labor are required for creating the annotations. A potential alternative is to reuse existing corpora from other institutions by pooling with local corpora, for training machine taggers. In this paper we have investigated the latter approach by pooling corpora from 2010 i2b2/VA NLP challenge and Mayo Clinic Rochester, to evaluate taggers for recognition of medical problems. The corpora were annotated for medical problems, but with different guidelines. The taggers were constructed using an existing tagging system MedTagger that consisted of dictionary lookup, part of speech (POS) tagging and machine learning for named entity prediction and concept extraction. We hope that our current work will be a useful case study for facilitating reuse of annotated corpora across institutions.We found that pooling was effective when the size of the local corpus was small and after some of the guideline differences were reconciled. The benefits of pooling, however, diminished as more locally annotated documents were included in the training data. We examined the annotation guidelines to identify factors that determine the effect of pooling.The effectiveness of pooling corpora, is dependent on several factors, which include compatibility of annotation guidelines, distribution of report types and size of local and foreign corpora. Simple methods to rectify some of the guideline differences can facilitate pooling. Our findings need to be confirmed with further studies on different corpora. To facilitate the pooling and reuse of annotated corpora, we suggest that - i) the NLP community should develop a standard annotation guideline that addresses the potential areas of guideline differences that are partly identified in this paper; ii) corpora should be annotated with a two-pass method that focuses first on concept recognition, followed by normalization to existing ontologies; and iii) metadata such as type of the report should be created during the annotation process.
| S-EPMC3599895 | biostudies-other
Project description:Herein, we present high-yielding, concise access to a set of xanthenium-derived, water-soluble, low-molecular-weight photocages allowing light-controlled cargo release in the green to red region. Very importantly, these new photocages allow installation of various payloads through ester, carbamate, or carbonate linkages even at the last stage of the synthesis. Payloads were uncaged with high efficiency upon green, orange, or red light irradiation, leading to the release of carboxylic acids, phenols, and amines. The near-ideal properties of a carboxanthenium derivative were further evaluated in the context of light-controlled drug release using a camptothecin-derived chemotherapeutic drug, SN38. Notably, the caged drug showed orders of magnitude lower efficiency in cellulo, which was reinstated after red light irradiation. The presented photocages offer properties that facilitate the translation of photoactivated chemotherapy toward clinical applications.
| S-EPMC9951246 | biostudies-literature
Project description:BackgroundConcept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.ObjectivesIn this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications.MethodsBased on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library.ResultsA total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.
| S-EPMC7746475 | biostudies-literature
Project description:ImportanceThe high cost of cancer medicines is a public health challenge. Policy makers in the US and Europe are debating reforms to drug pricing that would cover both the prices of new medicines when entering the market and price increases after they are launched.ObjectiveTo assess launch prices, postlaunch price changes, and clinical benefit of cancer drugs in the US compared with 3 European countries (England, Germany, and Switzerland).Design, setting, and participantsThis economic evaluation identified all new drugs that were approved for use in the US, England, Germany, and Switzerland with initial indications for treatment of adult solid tumor and hematologic cancers. Analysis included drugs approved by the US Food and Drug Administration between January 1, 2009, and December 31, 2019, and by the European Medicines Agency and Swissmedic until December 31, 2019. Prices were adjusted for currency and inflation. Clinical benefit of drugs indicated for solid tumors was assessed using the American Society of Clinical Oncology Value Framework and European Society for Medical Oncology Magnitude of Clinical Benefit Scale. Using Spearman rank correlation coefficients, correlations between clinical benefit and launch prices and postlaunch price changes for each country were evaluated.Main outcomes and measuresLaunch prices, postlaunch price changes, and clinical benefit of cancer drugs.ResultsThe cohort included 65 drugs: 47 (72%) approved for solid tumors and 18 (28%) for hematologic cancers. In all countries, the lowest median monthly treatment costs at launch were greater in 2018-2019 vs 2009-2010: $14 580 vs $5790 in the US, $5888 vs $4289 in Germany, $6593 vs $5784 in Switzerland, and $6867 vs $3939 in England. Between 2009 and 2019, 48 of 65 (74%) cancer drugs had price increases in the US that were greater than inflation. Only 1 of 62 (2%) drugs in England, 0 of 60 drugs in Germany, and 7 of 56 drugs (13%) in Switzerland had a median price increase greater than inflation. There were no associations between launch prices or postlaunch price changes and clinical benefit in any assessed country.Conclusions and relevanceDuring this economic evaluation study period, launch prices of cancer drugs were substantially higher in the US than in the assessed similar high-income European countries, a gap that increased in the years after approval. Cancer drug prices frequently increased faster than inflation in the US but decreased on inflation-adjusted terms in Europe. Price changes were not associated with clinical benefit in any country.
| S-EPMC8251654 | biostudies-literature
Project description:OBJECTIVE:Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (eg, ELMo, BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText). MATERIALS AND METHODS:Both off-the-shelf, open-domain embeddings and pretrained clinical embeddings from MIMIC-III (Medical Information Mart for Intensive Care III) are evaluated. We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings and compare these on 4 concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015. We also analyze the impact of the pretraining time of a large language model like ELMo or BERT on the extraction performance. Last, we present an intuitive way to understand the semantic information encoded by contextual embeddings. RESULTS:Contextual embeddings pretrained on a large clinical corpus achieves new state-of-the-art performances across all concept extraction tasks. The best-performing model outperforms all state-of-the-art methods with respective F1-measures of 90.25, 93.18 (partial), 80.74, and 81.65. CONCLUSIONS:We demonstrate the potential of contextual embeddings through the state-of-the-art performance these methods achieve on clinical concept extraction. Additionally, we demonstrate that contextual embeddings encode valuable semantic information not accounted for in traditional word representations.
| S-EPMC6798561 | biostudies-literature
Project description:Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type "clinical trials" to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task. The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged F-score for exact match increased from 80.3% to 82.3% and the micro-averaged F-score based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data.
| S-EPMC3272090 | biostudies-literature