•Organ-on-a-chip technology has the potential to improve the success of drug discovery.•Environmental cues and inter-organ communication increase physiological relevance.•Organotypic effects depend ...on model composition and on chip sensing technology.•The gain of confidence for human translation of ‘on-chip’ effects is emerging.•Multi-disciplinary networks are driving the evolution of organ-on-a-chip approaches.
Organs-on-a-chip (OOAC) are research platforms containing cellular models designed to recapitulate relevant biological cues and, in some cases, enable communication between ‘on-chip’ connected organs. With enhanced physiological relevance, improvements in predictivity of the efficacy and toxicity of test compounds are anticipated. However, there are challenges to demonstrate the ‘gain of confidence’ of this technology for patient benefit. Translational challenges, the opportunities and deficiencies of the organ models, their intercommunication and the platform technology are all issues to be resolved. Sensitive, real-time detection technologies and data-rich readouts are needed to understand OOAC biology. Thus, the validation of normal and disease biology on chip, and modelling to translate these data to patients, will help position this technology in mainstream drug discovery.
•Open innovation was born from a triple helix of paradigm shifts.•The zeitgeist for open innovation is now at a crucial inflection point.•The sensitivity of proprietary structure activity data ...remains a roadblock.•General schemata are summarized for a protected open innovation (POI) model.•The POI model is consistent with the academic and industrial missions.•POI is a path to achieving the ‘FIPNet’ model for transforming pharmaceutical R&D.
Open innovation in pharmaceutical R&D evolved from a triple helix of convergent paradigm shifts in academic, industrial and government research sectors. The birth of the biotechnology sector catalyzed shifts in location dynamics that led to the first wave of open innovation in pharmaceutical R&D between big pharma and startup companies. The National Institutes of Health (NIH) Roadmap was a crucial inflection point that set the stage for a new wave of open innovation models between pharmaceutical companies and universities that have the potential to transform the pharmaceutical R&D landscape. We highlight the attributes of leading protected open innovation models that foster the sharing of proprietary small molecule collections by lowering the risk of premature escape of intellectual property, particularly structure–activity data.
Game-changing, public–private partnering models in pharmaceutical R&D are discussed from a historical and forward-looking perspective, highlighting crucial inflection points that caused transformative changes in academic, government and industrial R&D sectors.
Pattern transformation with DNA circuits Chirieleison, Steven M; Allen, Peter B; Simpson, Zack B ...
Nature chemistry,
12/2013, Letnik:
5, Številka:
12
Journal Article
Recenzirano
Odprti dostop
Readily programmable chemical networks are important tools as the scope of chemistry expands from individual molecules to larger molecular systems. Although many complex systems are constructed using ...conventional organic and inorganic chemistry, the programmability of biological molecules such as nucleic acids allows for precise, high-throughput and automated design, as well as simple, rapid and robust implementation. Here we show that systematic and quantitative control over the diffusivity and reactivity of DNA molecules yields highly programmable chemical reaction networks (CRNs) that execute at the macroscale. In particular, we designed and implemented non-enzymatic DNA circuits capable of performing pattern-transformation algorithms such as edge detection. We also showed that it is possible to fine-tune and multiplex such circuits. We believe these strategies will provide programmable platforms on which to prototype CRNs, discover bottom-up construction principles and generate patterns in materials.
•Complex medicines with a novel delivery mechanism or chemistry are growing in clinical impact.•New chemistry and carrier technology will take complex medicines to unprecedented prominence.•These ...molecules challenge previous approaches to drug discovery and make imaging central to success.•Labelling and cell imaging are used to measure penetration, targeting and biological interactions.•PET and other tissue imaging informs the often-challenging translation into clinic.
A growing number and diversity of complex medicines is in development and reaching the market, with many of these medicines utilising innovative delivery technology to achieve appropriate biodistribution and exposure. Accurate assessment of biodistribution, cell penetration, internalised form, cargo release and efficacy are essential for the development of these medicines. Advanced imaging technologies, deploying different labelling techniques that allow the assessment of both carrier and cargo, are enabling in-depth analysis and providing a mechanistic understanding of each step in the drug delivery pathway. Translation across cell, tissue and whole-body settings using multiple imaging methods can provide decision-making information that is critical for clinical phase selection and for the development of complex medicines.
Abstract
Background
Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to ...evaluate the potential diagnostic performance of novel
1
H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading.
Methods
Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient.
Results
The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01).
Conclusion
The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.
Current sampling of genomic sequence data from eukaryotes is relatively poor, biased, and inadequate to address important questions about their biology, evolution, and ecology; this Community Page ...describes a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans.
Opening the lead generation toolbox Simpson, Peter B; Reichman, Melvin
Nature reviews. Drug discovery,
01/2014, Letnik:
13, Številka:
1
Journal Article
Recenzirano
Early-stage drug discovery is rapidly evolving into an endeavour in which scientific research communities in both the public and private sectors are finding new ways to share knowledge, expertise and ...resources. Here, we discuss some of the challenges for such collaborations and highlight examples in which traditional barriers such as intellectual property concerns have been addressed.
•Novel DTI parameters provide improved sensitivity and specificity in characterization and discrimination of prostate cancer tissues in vivo.•Quantitative DTI parameters have the potential to provide ...imaging biomarkers in the detection and characterization of prostate cancer with perineural invasion.•Using DTI tractography, higher fiber tract density was observed in advanced prostate cancer in vivo, in agreement with ex vivo neurogenesis.
This study is aimed at evaluating the potential role of quantitative magnetic resonance diffusion tensor imaging (DTI) and tractography parameters in the detection and characterization of peripheral zone prostate cancer with a particular attention for fiber tract density.
DTI was acquired from eleven high risk, transrectal ultrasound (TRUS)-guided biopsy proven prostate cancers with perineural invasion (histological Gleason score ≥ 7) on a 3 T magnet. Twenty parameters derived from DTI were quantified in cancer and healthy regions of the prostate. In addition, fiber tract density in normal versus cancer tissues was also calculated using DTI tractography. Support vector machine with a radial basis function kernel and area under receiver operator characteristic (ROC) were used to describe and compare the diagnostic performance of combined fractional anisotropy (FA) and mean diffusivity (MD) and other statistically significant DTI parameters. Spearman correlation analysis between DTI parameters and Gleason scores was conducted.
Eighteen DTI parameters yielded statistically significant differences between cancer and healthy regions (p-value < 0.05). The ROC curve of all statistically significant DTI parameters between cancer and healthy regions was higher than the area under ROC curve using FA + MD alone (95% confidence interval = 0.988, range = 0.975–1.00) vs (95% confidence interval = 0.935, range = 0.898-0.999), respectively (p-value < 0.05). Fiber tract density was also found to be higher in cancer than in healthy tissues (+38.22%, p-value = 0.010) and may be related to the increase in nerve and vascular density reported in prostate cancer. The linear and relative anisotropy were highly correlated with Gleason score (Spearman correlation factor r = 0.655, p-value = 0.001 and r = 0.667, p-value < 0.001, respectively).
DTI has the potential to provide imaging biomarkers in the detection and characterization of prostate cancer. Novel quantitative parameters derived from DTI and DTI tractography, including fiber tract density, support the use of DTI in the assessment of high grade prostate cancer.
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