Efficacy of Internet-based rumination-focused cognitive behavioral therapy and mindfulness-based intervention with guided support in reducing risks of depression and anxiety: A randomized controlled trial

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: Data are only available upon requestRumination and worry are common risk factors of depression and anxiety. Internet-based transdiagnostic interventions targeting individuals with these specific risks may be an effective way to prevent depression and anxiety. This three-arm randomized controlled trial compared the efficacy of Internet-based rumination-focused cognitive behavioral therapy (RFCBT), mindfulness-based intervention (MBI), and psychoeducation (EDU) control among 256 at-risk individuals. Participants' levels of rumination, worry, depressive, and anxiety symptoms were assessed at post-intervention (6 weeks), 3-month, and 9-month follow-ups. Linear mixed model analysis results showed similar levels of improvement in all outcomes across the three conditions. Changes in rumination differed comparing RFCBT and MBI, where a significant reduction in rumination was noted at a 3-month follow-up among participants in RFCBT, and no significant long-term effect among participants in MBI was noted at a 9-month follow-up. All three conditions showed similar reductions in risks and symptoms, implying that the two active interventions were not superior to EDU control. The high attrition at follow-ups suggested a need to exercise caution when interpreting the findings. Future studies should tease apart placebo effect and identify ways to improve adherence.Health and Medical Research Fund of Hong Kong SA

The role of machine learning in neuroimaging for drug discovery and development

Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. In this review, we focus on machine learning approaches for neuroimaging which enable us to make predictions at the individual level based on the distributed effects across the whole brain. Crucially, these approaches can be trained on data from one study and applied to an independent study and, unlike group-level statistics, can be readily use to assess the generalisability to unseen data. In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study? (2) What should I measure and when should I measure it? (3) How does the pharmacological agent behave using an experimental medicine model?, and (4) How does a compound differ from and/or resemble existing compounds? Specifically, we present studies from the literature and we suggest areas for the focus of future development. Further refinement and tailoring of machine learning techniques may help realise their tremendous potential for drug discovery and drug validation.</p

Engineering patient-on-a-chip models for personalized cancer medicine

Traditional in vitro and in vivo models typically used in cancer research have demonstrated a low predictive power for human response. This leads to high attrition rates of new drugs in clinical trials, which threaten cancer patient prognosis. Tremendous efforts have been directed towards the development of a new generation of highly predictable preclinical models capable to reproduce in vitro the biological complexity of the human body. Recent advances in nanotechnology and tissue engineering have enabled the development of predictive organs-on-a-chip models of cancer with advanced capabilities. These models can reproduce in  vitro the complex three-dimensional physiology and interactions that occur between organs and tissues in vivo, offering multiple advantages when compared to traditional models. Importantly, these models can be tailored to the biological complexity of individual cancer patients resulting into biomimetic and personalized cancer patient-on-a-chip platforms. The individualized models provide a more accurate and physiological environment to predict tumor progression on patients and their response to drugs. In this chapter, we describe the latest advances in the field of cancer patient-on-a-chip, and discuss about their main applications and current challenges. Overall, we anticipate that this new paradigm in cancer in vitro models may open up new avenues in the field of personalized â cancer â medicine, which may allow pharmaceutical companies to develop more efficient drugs, and clinicians to apply patient-specific therapies. The authors acknowledge the financial support from the European Union Framework Programme for Research and Innovation Horizon 2020 on Forefront Research in 3D Disease Cancer Models as in vitro Screening Technologies (FoReCaST) under grant agreement no. 668983. D.C. and S.C.K also acknowledge the support from the Portuguese Foundation for Science and Technology (FCT) under the scope of the project Modelling Cancer Metastasis into the Human Microcirculation System using a Multiorgan-on-a-Chip Approach (2MATCH) (02/SAICT/2017 – n° 028070) funded by the Programa Operacional Regional do Norte supported by FEDER. Conflicts of interest: none