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Gardenia jasminoides Ellis, a staple in herbal medicine, has long been esteemed for its purported hepatoprotective properties. Its primary bioactive constituent, geniposide, has attracted considerable scientific interest owing to its multifaceted therapeutic benefits across various health conditions. However, recent investigations have unveiled potential adverse effects associated with its metabolite, genipin, particularly at higher doses and prolonged durations of administration, leading to hepatic injury. Determining the optimal dosage and duration of geniposide administration while elucidating its pharmacological and toxicological mechanisms is imperative for safe and effective clinical application. This study aimed to evaluate the safe dosage and administration duration of geniposide in mice and investigate its toxicological mechanisms within a comprehensive dosage-duration-efficacy/toxicity model. Four distinct mouse models were employed, including wild-type mice, cholestasis-induced mice, globally farnesoid X-activated receptor (FXR) knock out mice, and high-fat diet-induced (HFD) NAFLD mice. Various administration protocols, spanning one or four weeks and comprising two or three oral doses, were tailored to each model's requirements. Geniposide has positive effects on bile acid and lipid metabolism at doses below 220 mg/kg/day without causing liver injury in normal mice. However, in mice with NAFLD, this dosage is less effective in improving liver function, lipid profiles, and bile acid metabolism compared to lower doses. In cholestasis-induced mice, prolonged use of geniposide at 220 mg/kg/day worsened liver damage. Additionally, in NAFLD mice, this dosage of geniposide for four weeks led to intestinal pyroptosis and liver inflammation. These results highlight the lipid-lowering and bile acid regulatory effects of geniposide, but also warn of potential negative impacts on intestinal epithelial cells, particularly with higher doses and longer treatment durations. Therefore, achieving optimal therapeutic results requires a decrease in treatment duration as the dosage increases, in order to maintain a balanced approach to the use of geniposide in clinical settings.

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One of the primary objectives of a dose-finding trial for novel anti-cancer agent combination therapies, such as molecular targeted agents and immune-oncology therapies, is to identify optimal dose combinations that are tolerable and therapeutically beneficial for subjects in subsequent clinical trials. The goal differs from that of a dose-finding trial for traditional cytotoxic agents, in which the goal is to determine the maximum tolerated dose combinations. This paper proposes the new design, named 'BOIN-ETC' design, to identify optimal dose combinations based on both efficacy and toxicity outcomes using the waterfall approach. The BOIN-ETC design is model-assisted, so it is expected to be robust, and straightforward to implement in actual oncology dose-finding trials. These characteristics are quite valuable from a practical perspective. Simulation studies show that the BOIN-ETC design has advantages compared with the other approaches in the percentage of correct optimal dose combination selection and the average number of patients allocated to the optimal dose combinations across various realistic settings.

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Azacitidine (Aza) is a mainstay of treatment for patients with acute myeloid leukaemia (AML) ineligible for induction chemotherapy and other high-risk myelodysplastic syndromes (MDS). Only half of patients respond, and almost all will eventually relapse. There are no predictive markers of response to Aza. Aza is detoxified in the liver by cytidine deaminase (CDA). Here, we investigated the association between CDA phenotype, toxicity and efficacy of Aza in real-world adult patients. Median overall survival (OS) was 15 months and 13 months in AML and high-risk MDS patients respectively. In addition, our data suggest that delaying Aza treatment was not associated with lack of efficacy and should not be considered a signal to switch to an alternative treatment. Half of the patients had deficient CDA activity (i.e. <2 UA/mg), with a lower proportion of deficient patients in MDS patients (34%) compared to AML patients (67%). In MDS patients, CDA deficiency correlated with longer landmark OS (14 vs. 8 months; p = 0.03), but not in AML patients. Taken together, our data suggest that CDA is an independent covariate and may therefore be a marker for predicting clinical outcome in MDS patients treated with Aza.

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In modern oncology drug development, adaptive designs have been proposed to identify the recommended phase 2 dose. The conventional dose finding designs focus on the identification of maximum tolerated dose (MTD). However, designs ignoring efficacy could put patients under risk by pushing to the MTD. Especially in immuno-oncology and cell therapy, the complex dose-toxicity and dose-efficacy relationships make such MTD driven designs more questionable. Additionally, it is not uncommon to have data available from other studies that target on similar mechanism of action and patient population. Due to the high variability from phase I trial, it is beneficial to borrow historical study information into the design when available. This will help to increase the model efficiency and accuracy and provide dose specific recommendation rules to avoid toxic dose level and increase the chance of patient allocation at potential efficacious dose levels. In this paper, we propose iBOIN-ET design that uses prior distribution extracted from historical studies to minimize the probability of decision error. The proposed design utilizes the concept of skeleton from both toxicity and efficacy data, coupled with prior effective sample size to control the amount of historical information to be incorporated. Extensive simulation studies across a variety of realistic settings are reported including a comparison of iBOIN-ET design to other model based and assisted approaches. The proposed novel design demonstrates the superior performances in percentage of selecting the correct optimal dose (OD), average number of patients allocated to the correct OD, and overdosing control during dose escalation process.

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ObjectiveTo explore the "efficacy-toxicity" association mechanisms of Tripterygium wilfordii polyglycoside tablets （TWPT） by establishing and analyzing an interaction network associated with the clinical efficacy of TWPT in the treatment of rheumatoid arthritis （RA） and TWPT-induced liver injury. MethodOn the basis of the TWPT efficacy-related gene expression profile and TWPT-induced liver injury-related protein expression profile which were both obtained from our clinical cohorts， the "efficacy-toxicity" association network of TWPT was constructed， and the key network targets were identified by calculating the topological values of the nodes， including the degree， closeness and betweenness. After that， the biological functions and pathways of the key network targets were investigated by enrichment analysis. ResultA total of 119 differentially expressed genes （58 up-regulated and 61 down-regulated） between RA patients with TWPT well and weak response were identified as TWPT efficacy-related genes by clinical transcriptomics， and 49 differentially expressed proteins （36 up-regulated and 13 down-regulated） were demonstrated to be TWPT-induced liver injury-related proteins by clinical proteomics. In addition， the clinical symptom enrichment analysis indicated that the TWPT efficacy-related genes were significantly associated with various clinical symptoms of arthralgia in traditional Chinese medicine and clinical phenotypes of modern medicine， and most of the TWPT-induced liver injury-related proteins were involved in digestive system abnormalities. Therefore， the aforementioned multi-omics data represented the main clinical symptoms of TWPT treating RA and inducing liver injury. Mechanically， the "efficacy-toxicity" association network revealed that both TWPT efficacy-related genes and TWPT-induced liver injury-related core proteins were involved in the "immune-inflammatory" imbalance， especially playing an important role in neutrophil degranulation， complement cascade reaction， and immune-inflammatory response mediated by protein post-translational modification. Notably， the above genes and proteins were also enriched in various signaling pathways related to cell proliferation and cell cycle regulation， such as RAS and mitogen-activated protein kinase （MAPK） signaling pathway， and in several liver functional processes， such as glycogen metabolism and redox reaction. ConclusionThis study systematically explained the "efficacy-toxicity" association characteristics and molecular mechanisms of TWPT by applying a research strategy integrating clinical phenomics， transcriptomics and proteomics， laying a good data foundation for exploring the "efficacy enhancing and toxicity-reducing" mechanisms of TWPT.

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Traditional Chinese medicine (TCM) usually acts in the form of compound prescriptions in the treatment of complex diseases. The herbs contained in each prescription have the dual nature of efficiency and toxicity due to their complex chemical component, and the principle of prescription is usually to increase efficiency and reduce toxicity. At present, the studies on prescriptions have mainly focused on the consideration of the material basis and possible mechanism of the action mode, but the quantitative research on the compatibility rule of increasing efficiency and reducing toxicity is still the tip of the iceberg. With the extensive application of computational pharmacology technology in the research of TCM prescriptions, it is possible to quantify the mechanism of synergism and toxicity reduction of the TCM formula. Currently, there are some classic drug pairs commonly used to treat complex diseases, such as Tripterygium wilfordii Hook. f. with Lysimachia christinae Hance for lung cancer, Aconitum carmichaelii Debeaux with Glycyrrhiza uralensis Fisch. in the treatment of coronary heart disease, but there is a lack of systematic quantitative analysis model and strategy to quantitatively study the compatibility rule and potential mechanism of synergism and toxicity reduction. To address this issue, we designed an integrated model which integrates matrix decomposition and shortest path propagation, taking into account both the crosstalk of the effective network and the propagation characteristics. With the integrated model strategy, we can quantitatively detect the possible mechanisms of synergism and attenuation of Tripterygium wilfordii Hook. f. and Lysimachia christinae Hance in the treatment of lung cancer. The results showed the compatibility of Tripterygium wilfordii Hook. f. and Lysimachia christinae Hance could increase the efficacy and decrease the toxicity of lung cancer treatment through MAPK pathway and PD-1 checkpoint pathway in lung cancer.

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Hydrogen sulfide is one of the most important signal transduction molecules in the body. Its anabolism and catabolism in the gastrointestinal tract(GT) are extremely high, and its role in the physiological and pathological process of the GT is fairly complicated. The study reviewed recent literature on hydrogen sulfide and GT, and proposed that hydrogen sulfide exerted dual modulating effects in the GT; specifically, it promoted the functions of the GT at low concentrations while damaged the GT at high concentrations. Hydrogen sulfide donors or metabolic modifiers exerted their therapeutic effects by restoring the metabolic homeostasis of hydrogen sulfide, and extended their efficacy to other tissues through hydrogen sulfide related gut-axis. Additionally, drugs could deviate hydrogen sulfide metabolism from the normal state due to their instability of structure, local over exposure and/or excessive pharmacological effects, thus inducing toxic and side effects or transforming therapeutic effects into toxic and side effects. This study provided references for the deep research on physiological and pathological mechanisms of hydrogen sulfide and facilitated the development of hydrogen sulfide-related drugs and discovery of their toxicity and efficacy mechanism.

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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.

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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.

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Aim To explore the efficacy-toxicity of Tripterygium wilfordii Hook.f.in intervention of lupus nephritis by the method of network pharmacology.Methods Firstly, the active components were searched and the action targets of Tripterygium wilfordii Hook.f.were predicted through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP).Secondly, the target genes of LN were collected through CoolGeN, OMIM and Gene Cards databases.And then, it was mapped to Tripterygium wilfordii Hook.f.targets and the drug component-disease target interaction network was constructed with Cytoscape software, and STRING database was used to analyze the protein interaction network.Finally, the common targets were analyzed by GO and KEGG pathway enrichment analysis using DAVID database to explore the potential mechanism of Tripterygium wilfordii Hook.f.in the treatment of LN.Results A total of 52 active components of Tripterygium wilfordii Hook.f.and 38 targets for the treatment of LN were screened.Most of the components had potential therapeutic effects on LN, but the effects of triptolide, tripterine, kaempferol and β-sitosterol may not be conducive to the improvement of LN.The results of KEGG analysis showed that efficacy-toxicity mainly involved NOD-like receptor signaling pathway, p53 signaling pathway, Toll-like receptor signal pathway and so on.Conclusions Tripterygium wilfordii Hook.f.plays the efficacy-toxicity effect on LN by regulating immune inflammation, cell proliferation and apoptosis, and its overall intervention effect needs further experimental study.

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Bacterial ß-glucuronidase enzymes (BGUSs) are at the interface of host-microbial metabolic symbiosis, playing an important role in health and disease as well as medication outcomes (efficacy or toxicity) by deconjugating a large number of endogenous and exogenous glucuronides. In recent years, BGUSs inhibition has emerged as a new approach to manage diseases and medication therapy and attracted an increasing research interest. However, a growing body of evidence underlines great genetic diversity, functional promiscuity and varied inhibition propensity of BGUSs, which have posed big challenges to identifying BGUSs involved in a specific pathophysiological or pharmacological process and developing effective inhibition. In this article, we offered a general introduction of the function, in particular the physiological, pathological and pharmacological roles, of BGUSs and their taxonomic distribution in human gut microbiota, highlighting the structural features (active sites and adjacent loop structures) that affecting the protein-substrate (inhibitor) interactions. Recent advances in BGUSs-mediated deconjugation of drugs and carcinogens and the discovery and applications of BGUS inhibitors in management of medication therapy, typically, irinotecan-induced diarrhea and non-steroidal anti-inflammatory drugs (NSAIDs)-induced enteropathy, were also reviewed. At the end, we discussed the perspectives and the challenges of tailoring BGUS inhibition towards precision medicine.

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A utility-based Bayesian population finding (BaPoFi) method was proposed by Morita and Müller to analyze data from a randomized clinical trial with the aim of identifying good predictive baseline covariates for optimizing the target population for a future study. The approach casts the population finding process as a formal decision problem together with a flexible probability model using a random forest to define a regression mean function. BaPoFi is constructed to handle a single continuous or binary outcome variable. In this paper, we develop BaPoFi-TTE as an extension of the earlier approach for clinically important cases of time-to-event (TTE) data with censoring, and also accounting for a toxicity outcome. We model the association of TTE data with baseline covariates using a semiparametric failure time model with a Pólya tree prior for an unknown error term and a random forest for a flexible regression mean function. We define a utility function that addresses a trade-off between efficacy and toxicity as one of the important clinical considerations for population finding. We examine the operating characteristics of the proposed method in extensive simulation studies. For illustration, we apply the proposed method to data from a randomized oncology clinical trial. Concerns in a preliminary analysis of the same data based on a parametric model motivated the proposed more general approach.

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In many settings, including oncology, increasing the dose of treatment results in both increased efficacy and toxicity. With the increasing availability of validated biomarkers and prediction models, there is the potential for individualized dosing based on patient specific factors. We consider the setting where there is an existing dataset of patients treated with heterogenous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. We propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. This approach maximizes overall efficacy at a prespecified constraint on overall toxicity. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose-biomarker interactions. To incorporate the large number of potential parameters, we use the LASSO method. We additionally constrain the dose effect to be non-negative for both efficacy and toxicity for all patients. Simulation studies show that the utility approach combined with any of the modeling methods can improve efficacy without increasing toxicity relative to fixed dosing. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy.

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One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.

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It is often of interest to explore how dose affects the toxicity and efficacy properties of a novel treatment. In oncology, efficacy is often assessed through response, which is defined by a patient having no new tumour lesions and their tumour size shrinking by 30%. Usually response and toxicity are analysed as binary outcomes in early phase trials. Methods have been proposed to improve the efficiency of analysing response by utilising the continuous tumour size information instead of dichotomising it. However, these methods do not allow for toxicity or for different doses. Motivated by a phase II trial testing multiple doses of a treatment against placebo, we propose a latent variable model that can estimate the probability of response and no toxicity (or other related outcomes) for different doses. We assess the confidence interval coverage and efficiency properties of the method, compared to methods that do not use the continuous tumour size, in a simulation study and the real study. The coverage is close to nominal when model assumptions are met, although can be below nominal when the model is misspecified. Compared to methods that treat response as binary, the method has confidence intervals with 30-50% narrower widths. The method adds considerable efficiency but care must be taken that the model assumptions are reasonable.

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BACKGROUND: Phase I and Phase II clinical trials aim at identifying a dose that is safe and active. Both phases are increasingly combined. For Phase I/II trials, two main types of designs are debated: a dose-escalation stage to select the maximum tolerated dose, followed by an expansion cohort to investigate its activity (dose-escalation followed by an expansion cohort), or a joint modelling to identify the best trade-off between toxicity and activity (efficacy-toxicity). We explore this question in the context of a paediatric Phase I/II platform trial. METHODS: In series of simulations, we assessed the operating characteristics of dose-escalation followed by an expansion cohort (DE-EC) designs without and with reassessment of the maximum tolerated dose during the expansion cohort (DE-ECext) and of the efficacy-toxicity (EffTox) design. We investigated the probability to identify an active and tolerable agent, that is, the percentage of correct decision, for various dose-toxicity activity scenarios. RESULTS: For a large therapeutic index, the percentage of correct decision reached 96.0% for efficacy-toxicity versus 76.1% for dose-escalation followed by an expansion cohort versus 79.6% for DE-ECext. Conversely, when all doses were deemed not active, the percentage of correct decision was 47% versus 55.9% versus 69.2%, respectively, for efficacy-toxicity, dose-escalation followed by an expansion cohort and DE-ECext. Finally, in the case of a narrow therapeutic index, the percentage of correct decision was 48.0% versus 64.3% versus 67.2%, respectively, efficacy-toxicity, dose-escalation followed by an expansion cohort and DE-ECext. CONCLUSION: As narrow indexes are common in oncology, according to the present results, the sequential dose-escalation followed by an expansion cohort is recommended. The importance to re-estimate the maximum tolerated dose during the expansion cohort is confirmed. However, despite their theoretical advantages, Phase I/II designs are challenged by the variations in populations between the Phase I and the Phase II parts and by the lagtime in the evaluation of toxicity and activity.

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Objective: To explore the “efficacy-toxicity” targets, pathways, and mechanism of Xiaochaihu Decoction in the treatment of hepatitis based on the ADR information excavating of drug-induced liver injury in the treatment of hepatitis with Xiaochaihu Decoction. Methods: Taking Xiaochaihu Decoction as the research object, the integrated pharmacology platform was used to analyze and predict key targets and pathways. Cytoscape software was used to collate and analyze key targets and pathways to obtain co-participation pathways and mechanisms of “efficacy-toxicity”. Results: The analysis of the effect of Xiaochaihu Decoction for liver injury in the treatment of hepatitis showed that there were 40 common targets such as HADHA, HADH, NSDHL, ADH1A, ALDH 3A2, and GCK among the top 100 candidate targets with degree value. The chemical components jointly participated in the network of “effect-toxicity” which belonged to Xiaochaihu Decoction including 27 components in Bupleuri Radix, 74 components in Ginseng Radix, 13 components in Pinelliae Rhizoma, 8 components in Zingiberis Rhizoma, 32 components in Glycyrrhizae Radix et Rhizoma, 39 components in Jujubae Fructus, and 33 components in Scutellaria baicalensis. A total of 226 components were involved in the “effect-toxicity” process through endocrine and metabolic diseases, non-alcoholic fatty liver disease (NAFLD), estrogen signaling pathway, neurodegenerative diseases, nervous system, chemokine signaling pathway, and endocrine system. Conclusion: The “efficacy-toxicity” effect of Xiaochaihu Decoction on liver injury in the treatment of hepatitis may be closely related to Ras/Raf/MEK/ERK, NF-B, and PI3K/AKT signal transduction pathway. It plays a role by regulating cell proliferation, cell apoptosis and the expression of inflammatory factors, which provides a theoretical basis for further pharmacological and toxicological verification and chemical separation research. It plays a role by regulating cell proliferation, cell apoptosis and the expression of inflammatory factors.

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One of the main purposes of a phase I dose-finding trial in oncology is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent phase II and III trials. Many phase I dose-finding methods based solely on toxicity considerations have been proposed under the assumption that both toxicity and efficacy monotonically increase with the dose level. Such an assumption may not be necessarily the case, however, when evaluating the OD for molecular targeted, cytostatic, and biological agents, as well as immune-oncology therapy. To address this issue, we extend the Bayesian optimal interval (BOIN) design, which is nonparametric and thus does not require the assumption used in model-based designs, in order to identify an OD based on both efficacy and toxicity outcomes. The new design is named "BOIN-ET." A simulation study is presented that includes a comparison of this proposed method to the model-based approaches in terms of both efficacy and toxicity responses. The simulation shows that BOIN-ET has advantages in both the percentages of correct ODs selected and the average number of patients allocated to the ODs across a variety of realistic settings.

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Based on the relation of efficacy and toxicity, this study mined the dosage rules and characteristics of Aconitum herbs in oral prescriptions from 48 traditional ancient books from Eastern Han dynasty to Qing dynasty, to provide the basis for strengthening the clinical risk pharmacovigilance. In the 48 traditional ancient books, 4 521 prescriptions with clear daily oral dosage were included to establish a database. SPSS 20.0 software was used for statistics and analysis of the daily dosage characteristics with different kinds of herbs, indications, dose forms, processing, use in special population, and other aspects. The results showed that 67% prescriptions contained Aconitum carmichaeli(Fuzi), and 90% of them was less than 14.87 g·d⁻¹; The dosage of A. carmichaeli(Chuanwu) and A. kusnezoffii(Caowu) were less than 3.14 g·d⁻¹. In the prescriptions for treating typhoid, epidemic, edema and phlegm, the dosage of Aconitum was larger. There dosage in the decoction and vinum was significantly higher than that in the pill and powder. With the dynastic evolution, the dosage of Aconitum herbal medicines prescriptions and the application percentage of superposition drug also had decreased. For the special populations that with different metabolism process, such as old people, children, pregnant and lactating women, the application of Aconitum was not only with relatively small ratio, but also with lower dose. Therefore, based on the data-mining of ancient books, the dosage of Aconitum should not exceed the limit prescribed by the current China Pharmacopoeia, and also should be strictly controlled by considering various factors, which will ensure the balance of efficacy and toxicity.

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INTRODUCTION: Polypharmacology has emerged as an essential paradigm for modern drug discovery process. Multiple lines of evidence suggest that agents capable of modulating multiple targets in a selective manner may offer also improved balance between therapeutic efficacy and safety compared to single-targeted agents. Areas covered: Herein, the authors review the recent progress made in experimental and computational strategies for addressing the critical challenges with rational discovery of selective multi-targeted agents within the context of polypharmacological modelling. Specific focus is placed on multi-targeted mono-therapies, although examples of combinatorial polytherapies are also covered as an important part of the polypharmacology paradigm. The authors focus mainly on anti-cancer treatment applications, where polypharmacology is playing a key role in determining the efficacy-toxicity trade-off of multi-targeting strategies. Expert opinion: Even though it is widely appreciated that complex polypharmacological interactions can contribute both to therapeutic and adverse side-effects, systematic approaches for improving this balance by means of integrated experimental-computational strategies are still lacking. Future developments will be needed for comprehensive collection and harmonization of systems-wide target selectivity data, enabling better utilization and control for multi-targeted activities in the drug development process. Additional areas of future developments include model-based strategies for drug combination screening and improved pre-clinical validation options with animal models.

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