Impact of distance between two hubs on the network coherence of tree networks

We study the impact of the distance between two hubs on network coherence defined by Laplacian eigenvalues.Network coherence is a measure of the extent of consensus in a linear system with additive noise.To obtain an exact determination of coherence based on the distance,we choose a family of tree networks with two hubs controlled by two parameters.Using the tree’s regular structure,we obtain analytical expressions of the coherences with regard to network parameters and the network size.We then demonstrate that a shorter distance and a larger difference in the degrees of the two hubs lead to a higher coherence.With the same network size and distance,the best coherence occurs in the tree with the largest difference in the hub’s degrees.Finally,we establish a correlation between network coherence and average path length and find that they behave linearly.

Why 90%of clinical drug development fails and how to improve it?

Ninety percent of clinical drug development fails despite implementation of many successful strategies,which raised the question whether certain aspects in target validation and drug optimization are overlooked?Current drug optimization overly emphasizes potency/specificity using structure-activityrelationship(SAR)but overlooks tissue exposure/selectivity in disease/normal tissues using structure-tissue exposure/selectivity—relationship(STR),which may mislead the drug candidate selection and impact the balance of clinical dose/efficacy/toxicity.We propose structure-tissue exposure/selectivity—activity relationship(STAR)to improve drug optimization,which classifies drug candidates based on drug’s potency/selectivity,tissue exposure/selectivity,and required dose for balancing clinical efficacy/toxicity.ClassⅠdrugs have high specificity/potency and high tissue exposure/selectivity,which needs low dose to achieve superior clinical efficacy/safety with high success rate.ClassⅡdrugs have high specificity/potency and low tissue exposure/selectivity,which requires high dose to achieve clinical efficacy with high toxicity and needs to be cautiously evaluated.ClassⅢdrugs have relatively low(adequate)specificity/potency but high tissue exposure/selectivity,which requires low dose to achieve clinical efficacy with manageable toxicity but are often overlooked.ClassⅣdrugs have low specificity/potency and low tissue exposure/selectivity,which achieves inadequate efficacy/safety,and should be terminated early.STAR may improve drug optimization and clinical studies for the success of clinical drug development.

Tumor-derived exosomes:Nanovesicles made by cancer cells to promote cancer metastasis

Nanomedicine usually refers to nanoparticles that deliver the functional drugs and siRNAs to treat cancer.Recent research has suggested that cancer cells can also make nanoparticles that also deliver functional molecules in promoting cancer metastasis,which is the leading cause of various cancer mortalities.This nanoparticle is called tumor-derived vesicles,or better-known as tumor-derived exosomes(TEXs).TEXs are nanoscale membrane vesicles(30 e140 nm)that are released continuously by various types of cancer cells and contain tumor-derived functional biomolecules,including lipids,proteins,and genetic molecules.These endogenous TEXs can interact with host immune cells and epithelial cells locally and systemically.More importantly,they can reprogram the recipient cells in favor of promoting metastasis through facilitating tumor cell local invasion,intravasation,immune evasion,extravasation,and survival and growth in distant organs.Growing evidence suggests that TEXs play a key role in cancer metastasis.Here,we will review the most recent findings of how cancer cells harness TEXs to promote cancer metastasis through modulating vascular permeability,suppressing systemic immune surveillance,and creating metastatic niches.We will also summarize recent research in targeting TEXs to treat cancer metastasis.

Structure-tissue exposure/selectivity relationship(STR) correlates with clinical efficacy/safety

Drug optimization, which improves drug potency/specificity by structure-activity relationship(SAR) and drug-like properties, is rigorously performed to select drug candidates for clinical trials. However,the current drug optimization may overlook the structure-tissue exposure/selectivity-relationship(STR) in disease-targeted tissues vs. normal tissues, which may mislead the drug candidate selection and impact the balance of clinical efficacy/toxicity. In this study, we investigated the STR in correlation with observed clinical efficacy/toxicity using seven selective estrogen receptor modulators(SERMs) that have similar structures, same molecular target, and similar/different pharmacokinetics. The results showed that drug’s plasma exposure was not correlated with drug’s exposures in the target tissues(tumor, fat pad, bone, uterus),while tissue exposure/selectivity of SERMs was correlated with clinical efficacy/safety. Slight structure modifications of four SERMs did not change drug’s plasma exposure but altered drug’s tissue exposure/selectivity.Seven SERMs with high protein binding showed higher accumulation in tumors compared to surrounding normal tissues, which is likely due to tumor EPR effect of protein-bound drugs. These suggest that STR alters drug’s tissue exposure/selectivity in disease-targeted tissues vs. normal tissues impacting clinical efficacy/toxicity. Drug optimization needs to balance the SAR and STR in selecting drug candidate for clinical trial to improve success of clinical drug development.