Recently, cannabinoids, such as cannabidiol (CBD) and Δ9 -tetrahydrocannabinol (THC), have been the subject of intensive research and heavy scrutiny. Cannabinoids encompass a wide array of organic molecules, including those that are physiologically produced in humans, synthesized in laboratories, and extracted primarily from the Cannabis sativa plant. These organic molecules share similarities in their chemical structures as well as in their protein binding profiles. However, pronounced differences do exist in their mechanisms of action and clinical applications, which will be briefly compared and contrasted in this review. The mechanism of action of CBD and its potential applications in cancer therapy will be the major focus of this review article.

In the era of precision medicine, cancer researchers and oncologists are eagerly searching for more realistic, cost effective, and timely tumor models to aid drug development and precision oncology. Tumor models that can faithfully recapitulate the histological and molecular characteristics of various human tumors will be extremely valuable in increasing the successful rate of oncology drug development and discovering the most efficacious treatment regimen for cancer patients. Two-dimensional (2D) cultured cancer cell lines, genetically engineered mouse tumor (GEMT) models, and patient-derived tumor xenograft (PDTX) models have been widely used to investigate the biology of various types of cancers and test the efficacy of oncology drug candidates. However, due to either the failure to faithfully recapitulate the complexity of patient tumors in the case of 2D cultured cancer cells, or high cost and untimely for drug screening and testing in the case of GEMT and PDTX, new tumor models are urgently needed. The recently developed patient-derived tumor organoids (PDTO) offer great potentials in uncovering novel biology of cancer development, accelerating the discovery of oncology drugs, and individualizing the treatment of cancers.In this review, we will summarize the recent progress in utilizing PDTO for oncology drug discovery. In addition, we will discuss the potentials and limitations of the current PDTO tumor models. K E Y W O R D S drug testing, patient derived tumor organoids, precision oncology, tumor models | 151 GRANAT eT Al. not be present in cells when grown in vivo. 4 Third, the growth medium used for culturing cancer cell lines is not able to completely mirror the conditions and environment that tumor cells naturally reside in. In vivo, tumor cells are surrounded by fibroblasts, blood vessels, and immune cells, and their collective interactions are important; this aspect is unfortunately missing in the cultured cancer cell lines. 5 Therefore, the in vitro cultured 2D cancer cell lines are the least faithful tumor model to be able to recapitulate patient tumors. By growing the established cancer cell lines in a three-dimensional (3D) environment, which mimics the in vivo extracellular matrix, the so called 3D cell culture moves a step closer to the in vivo tumors. 6However, the 3D cell culture still lacks the complex tissue hierarchy comparing to the primary tumors. 6 Therefore, the 3D cell culture is not ideal for investigating the tumor biology and testing oncology drugs. | Genetically engineered mouse tumor modelsDue to the aforementioned limitations, another commonly utilized model in cancer research is the genetically engineered mouse tumor (GEMT) model. In contrast to transplanting cancer cell lines into mice, which requires an immunocompromised status of the host mice to prevent rejection, GEMT is immunocompetent. 7 Therefore, GEMT can be potentially used for the investigation of immunotherapy. However, mouse tumor models often do not faithfully recapitulate the human cancers. Furthermore, generat...

Perception of limb position and motion combines sensory information from spindles in muscles that span one joint (monoarticulars) and two joints (biarticulars). This anatomical organization should create interactions in estimating limb position. We developed two models, one with only monoarticulars (MO Model) and one with monoarticulars and biarticulars (MB Model), to explore how biarticulars influence estimates of arm position in hand (x,y) and joint (shoulder,elbow) coordinates. In hand coordinates, both models predicted larger medial-lateral than proximal-distal errors, though the MB Model predicted that biarticulars would reduce this bias. In contrast, the two models made significantly different predictions in joint coordinates. The MO Model predicted that errors would be uniformly distributed because estimates of angles at each joint would be independent. In contrast, the MB Model predicted that errors would be coupled between the two joints, resulting in smaller errors for combinations of flexion or extension at both joints and larger errors for combinations of flexion at one joint and extension at the other joint. We also carried out two experiments to examine errors made by human subjects during an arm position matching task in which an robot passively moved one arm to different positions and the subjects moved their other arm to mirror-match each position. Errors in hand coordinates were similar to those predicted by both models. Critically, however, errors in joint coordinates were only similar to those predicted by the MB Model. These results highlight how biarticulars influence perceptual estimates of limb position by helping to minimize medial-lateral errors.

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