Nanoformulations are transforming our capacity to effectively deliver and treat a myriad of conditions. However, many nanoformulation approaches still suffer from high production complexity and low drug loading. One potential solution relies on harnessing co-assembly of drugs and small molecular excipients to facilitate nanoparticle formation through solvent exchange without the need for chemical synthesis, generating nanoparticles with up to 95% drug loading. However, there is currently no understanding which of the millions of possible combinations of small molecules can result in the formation of these nanoparticles. Here we report the development of a high-throughput screening platform coupled to machine learning to enable the rapid evaluation of such nanoformulations. Our platform identified 101 novel self-assembling drug nanoparticles from 2.1 million pairings derived from 788 candidate drugs with one of 2686 excipients, spanning treatments for multiple diseases and often harnessing well-known food additives, vitamins, or approved drugs as carrier materials -with potential for accelerated approval and translation. Given their long-term stability and potential for clinical impact, we further characterize novel sorafenib-glycyrrhizin and terbinafine-taurocholic acid nanoparticles ex vivo and in vivo. We anticipate that this platform could accelerate the development of safer and more efficacious nanoformulations with high drug loadings for a wide range of therapeutics.

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Complex 3D bioengineered tumour models provide the opportunity to better capture the heterogeneity of patient tumours. Patient-derived organoids are emerging as a useful tool to study tumour heterogeneity and variation in patient responses. Organoid cultures typically require a 3D microenvironment that can be manufactured easily to facilitate screening. Here we set out to create a high-throughput, "off-the-shelf" platform which permits the generation of organoid- containing microtissues for standard phenotypic bioassays and image-based readings. To achieve this, we developed the Scaffold-supported Platform for Organoid-based Tissues (SPOT) platform. SPOT is a 3D gel-embedded in vitro platform that can be produced in a 96- or 384-well plate format and enables the generation of flat, thin and dimensionally-defined microgels. SPOT has high potential for adoption due to its reproducible manufacturing methodology, compatibility with existing instrumentation, and reduced within-sample and between-sample variation, which can pose challenges to both data analysis and interpretation. Using SPOT we generate cultures from patient derived pancreatic ductal adenocarcinoma organoids and assess the cellular response to standard-of-care chemotherapeutic compounds, demonstrating our platform's usability for drug screening. We envision 96/384-SPOT will provide a useful tool to assess drug sensitivity of patient-derived organoids and easily integrate into the drug discovery pipeline.

Nanoformulations are transforming our capacity to effectively deliver and treat a myriad of conditions. However, many nanoformulation approaches still suffer from high production complexity and low drug loading. One potential solution relies on harnessing co-assembly of drugs and small molecular excipients to facilitate nanoparticle formation through solvent exchange without the need for chemical synthesis, generating nanoparticles with up to 95% drug loading. However, there is currently no understanding which of the millions of possible combinations of small molecules can result in the formation of these nanoparticles. Here we report the development of a high-throughput screening platform coupled to machine learning to enable the rapid evaluation of such nanoformulations. Our platform identified 101 novel self-assembling drug nanoparticles from 2.1 million pairings derived from 788 candidate drugs with one of 2686 excipients, spanning treatments for multiple diseases and often harnessing well-known food additives, vitamins, or approved drugs as carrier materials -with potential for accelerated approval and translation. Given their long-term stability and potential for clinical impact, we further characterize novel sorafenib-glycyrrhizin and terbinafine-taurocholic acid nanoparticles ex vivo and in vivo. We anticipate that this platform could accelerate the development of safer and more efficacious nanoformulations with high drug loadings for a wide range of therapeutics.

The success rate of bringing novel cancer therapies to the clinic remains extremely low due to the lack of relevant pre-clinical culture models that capture the complexity of human tumours. Patient-derived organoids have emerged as a useful tool to model patient and tumour heterogeneity to begin addressing this need. Scaling these complex culture models while enabling stratified analysis of different cellular sub-populations remains a challenge, however. One strategy to enable higher throughput organoid cultures that also enables easy image-based analysis is the Scaffold-supported Platform for Organoid-based Tissues (SPOT) platform. SPOT allows the generation of flat, thin and dimensionally-defined microtissues in both 96- and 384-well plate footprints and is compatible with tumour organoid culture and downstream image-based readouts. SPOT manufacturing is currently a manual process however, limiting the use of SPOT to perform larger-scale screening. In this study, we integrate and optimize an automation approach to generate tumour-mimetic 3D engineered microtissues in SPOT using a liquid handler, and show comparable within-sample and between-sample variation as the standard manual manufacturing process. Furthermore, we develop a liquid handler-supported whole-cell extraction protocol and as a proof-of-value demonstration, we generate 3D complex tissues containing different proportions of tumour and stromal cells and perform single-cell-based end-point analysis to demonstrate the impact of co-culture on the tumour cell population specifically. We also demonstrate we can incorporate primary patient-derived organoids into the pipeline to capture patient-level tumour heterogeneity. We envision that this automated workflow integrated with 96/384-SPOT and multiple cell types and patient-derived organoid models will provide opportunities for future applications in high-throughput screening for novel personalized therapeutic targets. This pipeline also allows the user to assess dynamic cell responses using high-content longitudinal imaging or downstream single-cell-based analyses.

Patient‐derived organoids have emerged as a useful tool to model tumour heterogeneity. Scaling these complex culture models while enabling stratified analysis of different cellular sub‐populations, however, remains a challenge. One strategy to enable higher throughput organoid cultures is the scaffold‐supported platform for organoid‐based tissues (SPOT). SPOT allows the generation of flat, thin, and dimensionally‐defined microtissues in both 96‐ and 384‐well plate footprints that are compatible with longitudinal image‐based readouts. SPOT is currently manufactured manually, however, limiting scalability. In this study, an automation approach to engineer tumour‐mimetic 3D microtissues in SPOT using a liquid handler is optimized and comparable within‐ and between‐sample variation to standard manual manufacturing is shown. Further, a liquid handler‐supported cell extraction protocol to support single‐cell‐based end‐point analysis using high‐throughput flow cytometry and multiplexed cytometry by time of flight is developed. As a proof‐of‐value demonstration, 3D complex tissues containing different proportions of tumour and stromal cells are generated to probe the reciprocal impact of co‐culture. It is also demonstrated that primary patient‐derived organoids can be incorporated into the pipeline to capture patient‐level tumour heterogeneity. It is envisioned that this automated 96/384‐SPOT workflow will provide opportunities for future applications in high‐throughput screening for novel personalized therapeutic targets.

This paper uses the Analytics Hierarchy Process to realize the analysis of cultural tourism competitiveness. First of all, the authors design the questionnaire and carry out the research on valid objects, which realizes the collection of valid information about influential factors on cultural tourism competitiveness. Then the authors categorize and draw out the concept of influential factors for cultural tourism development, determine the importance degree between the two factors through comparison, establish the Analytic Hierarchy Process of cultural tourism competitiveness, and then judge the validity of the analysis result through consistency test. The research results show that in the construction stage, in order to improve the cultural tourism competitiveness, it is necessary to adopt the mode of "moderate development" and "complete development" respectively according to the types of cultural tourism resources. When designing, it's possible to refer to the mature modes of historic sites and cultural towns. In addition, an effective way to realize users' cultural experience and obtain identity recognition should be provided.

Novel anticancer therapeutics are urgently required to meet the increasing global cancer burden associated with aging populations. The development of new drugs is hindered by high failure rates at clinical stages, which are partly attributable to inadequate screening strategies which rely heavily on the use of cancer cell lines cultured in 2D and animal models. Although each of these models has certain advantages, they generally fail to accurately represent the human pathophysiology of malignant tumors. Emerging tissue-engineered 3D cancer models designed to better mimic in vivo tumors have the potential to provide additional tools to complement those currently available to address this limitation and improve drug discovery and translation in the long run. To successfully develop and implement a 3D cancer model for drug screening, several key steps are necessary: selection of the tumor type and concept to be modeled, identification of the essential components and set up of the model, model validation, establishment of a scalable manufacturing and analysis pipeline, and selection of a drug library to perform the screen. In this chapter, we elaborate on and evaluate each of these decision steps, highlight the challenges associated with each step, and discuss opportunities for future research.