Time Of Drug Development Research Articles

Impact of Magnesium Stearate Content: Modeling of Drug Degradation Using a Modified Arrhenius Equation.

To accelerate drug development, the pharmaceutical industry is working to shorten and improve studies on stability. The Accelerated Stability Assessment Program (ASAP) incorporating the humidity-corrected Arrhenius equation as an accelerated methodology has been proposed for both drug substances and drug products. In this study, the effect of magnesium stearate (MgSt) content on the chemical stability of acetylsalicylic acid was evaluated as a model system of drug-excipient compatibility studies using ASAP. In the acetylsalicylic acid powder blends, temperature and humidity showed a first-order linear response to the natural logarithm of the reaction rate constant, and MgSt content also showed a first-order linear response. A polynomial model was built in which temperature, humidity, and MgSt content were independent each other. The fitting index of the model, the coefficient of determination, was 0.9567, which was a good fit. In the long-term stability study (25 °C/60% relative humidity, 6 months), there was good agreement in total between measured values and model-predicted values. Using this model, we inferred that the degradation rates were depended on MgSt content at the fixed temperature and humidity because the micro-environmental pH of the excipient was catalytically affected. Applying this model equation can significantly reduce the duration of formulation design and stability studies and save time and costs in drug development.

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

BackgroundIt often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time.MethodsIn this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.ResultsThe proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.ConclusionResults show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

Analysis of Pediatric Drug Approval Lag in Japan.

Drugs should be made available to all patients in a timely manner regardless of whether they are adults or children. In contrast to the United States (US) and the European Union (EU), no laws mandating pediatric drug development have been established in Japan. The objective of this study was to examine the current status and characteristics of pediatric drug development in Japan using information on the approval lag for pediatric indications between Japan and the EU. Drugs approved for pediatric indications between January 2007 and December 2018 in Japan were studied. The approval lag for pediatric indications between Japan and the EU was calculated and analyzed by approval time and the Anatomical Therapeutic Chemical (ATC) classification. Factors potentially affecting the approval lag, such as orphan drug status, company nationality, and clinical data package, were examined. The median approval lag for 105 drugs with pediatric indications in both Japan and the EU was 1017days (Japan was behind). The lag for ATC category B improved significantly after 2011, and for category L after 2015; the medians were less than half a year. The lag for drugs developed globally via multi-regional clinical trials was significantly shorter compared with drugs developed regionally. Global clinical trials are the most effective means of shortening approval lag time in pediatric drug development. Global development is making rapid progress for many adult diseases, thereby creating an environment for proactive participation in global clinical trials even for pediatric drugs. For further improvement, more active drug development for pediatric indication is recommended in tandem with the US and the EU.

Prediction of fasted and fed bioequivalence for immediate release drug products using physiologically based biopharmaceutics modeling (PBBM)

Bioequivalence studies are an integral part of clinical pharmacology strategy for drug development. Physiologically based biopharmaceutics modeling (PBBM) can be a helpful tool to assess potential bioequivalence risks and predict the outcome of bioequivalence studies. In this study, GastroPlus™ was used for virtual bioequivalence (VBE) assessment of 6 case studies which includes four BCS 2, and one each of BCS 1 and 3 molecules. The purpose was to investigate if bioequivalence in fed state can be accurately predicted based on model developed on data from bioequivalence study in fasted state and known food effect from clinical studies. Our results show that we were able to successfully predict passing (5 cases) and failed (1 case) bioequivalence studies. Ultimately, if there is confidence in such models, a case can be made to waive fed bioequivalence study, on a case-by-case basis (e.g. for BCS class 1 and 2 molecules with known food effect mechanism, reliable estimate of human pharmacokinetic parameters, and available in vivo data in fasted state for model verification). This has the potential to reduce clinical burden in drug development, increase confidence in pivotal BE studies and support regulatory applications such as justify waiving of BE study for Scale-Up and Post Approval Changes (SUPAC). Hence VBE can significantly reduce time and cost of drug development, as well as minimize drug exposure to healthy volunteers.

In-Silico Pharmacological and Molecular Docking Studies of Natural Inhibitors form Musa Spp. On Vaca Gene a Vacuolating Cytotoxin Autotransporter

The integration of computational with various bioinformatics software/tools/webservers and the molecular docking process is the current key point method in reducing the time for drug discovery and drug development from a Bioactive compound. Current Insilco Study considers the Bioactive Phytoconstituents from Musa species for our drug discovery process by using bioinformatics software to find out the analogs by following their related physicochemical properties for the structure-based drug design. The gastric bacterium H. pylori infect the gastric mucosa, and its eradication is associated with the prevention of ulcer reoccurrence. The significant Protein target from this species was a challenging task for finding, targeting, treating. Offering hope from the Bioactive compounds considered in line with already FDA approved Drug molecules against peptic ulcer causative organism’s special protein vacuolating cytotoxin autotransporter translated from vacA Gene. The recent challenge is to identify a drug moiety that will effectively work on the target protein. Usually, the Phytocompounds will not significantly cause a side effect. Sitosterol with docking score of -6.417 kcal/mol has promised to serve as vacuolating cytotoxin autotransporter inhibitor by blocking the autotransporter protein to the periplasmic space avoiding the toxin passage between periplasmic membrane which will avoid the gastric infections from H. pylori.

Update to Drugs, Devices, and the FDA: How Recent Legislative Changes Have Impacted Approval of New Therapies

SummaryTwo major legislative actions since 2015, the 21st Century Cures Act of 2016 and the U.S. Food and Drug Administration (FDA) Reauthorization Act of 2017, contain significant provisions that potentially streamline drug development times, and by extension, may reduce costs. Evidence suggests, however, that development times have already been significantly affected by previous legislation and FDA programs, through accelerated approval pathways and adoption of more flexible definitions of clinical evidence of efficacy. The COVID-19 pandemic is pushing researchers and commercial entities to further test the limits of drug and vaccine development times and approvals, at an as yet unknown level of risk to patients. COVID-19 drug and vaccine trials are even now making use of accelerated drug approval programs, blended trials, and adaptive trial design to accelerate approval of therapeutics in the pandemic.

Development Time and Patent Extension for Prescription Drugs in Canada: A Cohort Study.

The Comprehensive Economic and Trade Agreement between Canada and the European Union provides for an extension of Canadian patents for prescription drugs by up to 2 years. One of the arguments advanced for longer patent time is to compensate companies for the length of the overall drug development time (the time between patent application and market approval). This study investigates overall development time in Canada for different groups of drugs approved between January 1, 2009 and December 31, 2018 and how many of these drugs are eligible for up to 2 years of patent term extension. Based on a list of patents and dates of market approval, the change in overall development time for all drugs was calculated along with whether there were differences in development time between different groups of drugs. Using Canadian patent filing dates, overall development time for all drugs went from a mean of 2240 days (95% CI: 1832, 2648) in 2009 to 4197 days (95% CI: 3728, 4665) in 2018 (analysis of variance [ANOVA], P<.0001). Using first global patent filing dates, overall development time went from a mean of 4481 days (95% CI: 3053, 5908) in 2009 to 6298 days (95% CI: 4839, 7756) in 2018 (ANOVA, P=.0118). There was a statistically significant difference in the overall development mean time between small molecule drugs (3553, 95% CI: 3361, 3746) and biologics (3903, 95% CI: 3595, 4212), (t test, P=.0487) when using Canadian patent filing dates but not when first global patent filing dates were used. There was no statistically significant change in overall development time among drugs that were substantial, moderate or little to none additional therapeutic value compared to existing drugs. Out of 238 drugs, 218 (91.6%) would have been eligible for patent term extension with 195 (80.7%) eligible for the full 2 years. Patent term extension does not appear to be justified based on changes in overall development time, except possibly in the case of biologics. There are also trade offs in terms of increased expenditures that need to be considered if patent terms are lengthened.

The Financial Benefits of Faster Development Times: Integrated Formulation Development, Real-Time Manufacturing, and Clinical Testing.

Faster drug development times get new therapies to patients sooner and financially benefit drug developers by shortening the time between investment and returns and increasing the time on the market with intellectual property protection. The result is enhanced incentives to innovate. We provide a real-world example of the financial gains from quicker development using recent estimates of drug development costs, returns, and estimates of time reductions from an alternative early-stage drug development paradigm. We utilized data obtained from a drug development and manufacturing services organization to estimate the reduction in development time for drug sponsors from using an integrated platform of formulation development, real-time manufacturing, and clinical testing for 19 completed drug product development projects covering three key drug development activities (transitioning from first-in-human to proof-of-concept [FIH-PoC], modified release formulation development [MR], and enhanced solubility formulation development [ES]). A traditional drug development paradigm was taken as the base case and financial impacts of the alternative development program were determined relative to the base case. The total after-tax financial benefits of shorter development times from integrating formulation development, real-time manufacturing, and clinical testing when applied across a broad portfolio of investigational drugs ranged from $230.5 million to $290.1 million, $196.4 million to $247.5 million, and $102.6 million to $275.5 million, per approved new drug for FIH-PoC, MR, and ES applications, respectively (2018 dollars). For the data we examined, this integrated development model yielded substantial financial benefits over traditional drug development.

Constitutional legitimacy and overseas legislative trends in the use of health care data

다양하게 생성된 데이터를 수집·분석·활용하여 맞춤형 의료서비스를 하는 디지털 헬스케어는 4차 산업혁명 시대 핵심 산업분야로 부각되고 있다. 디지털 헬스케어 선점을 위한 글로벌 주도권 쟁탈전이 본격적으로 시작된 가운데 유통시장의 추세가 오프라인에서 온라인으로 급속히 이동하고 있는 상황이다. 우리나라 보건복지부도 개인맞춤의료 실현 및 미래 신성장 동력확보를 위해 정밀의료 기술개발을 추진하겠다는 포부를 밝혔다. 헬스케어서비스 발전을 위해서는 진료정보 뿐 아니라 태어나면서부터 가진 유전정보 또는 일상생활을 하는 모든 과정에서 생성되는 정보의 수집과 분석이 필요하다. 데이터 활용을 통한 의료산업의 진흥은 경제발전으로 이어질 뿐만 아니라, 의료서비스의 특성으로 말미암아 국민의 건강증진에 기여하고 헌법질서의 최고목표이자 가치인 인간의 존엄과 가치를 실현하는 방안이 될 수 있다. 헌법 제36조 제3항은 “모든 국민은 보건에 관하여 국가의 보호를 받는다.”고 규정하고 있고, 보건은 건강을 온전하게 잘 지키는 것으로 병의 진단과 치료를 넘어 질병의 예방으로 사람의 건강과 생명을 보호하고 증진하는 일을 이른다. 의료데이터의 활용을 전제로 하는 헬스케어서비스는 질병의 예방 및 정밀의료를 가능케하고 나아가 신약개발시간의 단축 등 국민의 건강을 온전하게 지키는 일에 도움이 될 것이다. 한편 과학기술의 진보는 산업발전을 수반하였고, 이는 경제적 측면에서 부의 증대현상을 가져왔다. 이는 국민의 삶의 질이나 행정의 효율성 등 국가운영 및 국민 생활에 지대한 영향을 미친다. 이에 따라 신산업 발전에 다량의 데이터 활용은 필수적이다. 미국, 일본, 독일, 핀란드 등 세계 각국에서는 의료데이터의 활용을 위한 별도의 법률을 제정하여 민감정보인 의료데이터의 안전한 활용방안을 모색하고 있다. 그러나 의료데이터는 정보의 민감성, 전문성으로 인하여 수집. 가공. 활용에 관한 특별한 취급과 관리가 필요하다. 개인정보보호와 데이터의 활용은 양자 모두 포기할 수 없는 가치이다. 법과 제도적 장치의 보완을 통해 데이터 오·남용에 대한 리스크를 줄여 국민들의 신뢰를 확보하여야 한다. 개인정보보호를 위한 법과 제도의 마련은 결국 정보의 활용을 막는 걸림돌로 작용하는 것이 아니라 정보의 안전한 활용에 대한 신뢰를 구축하는 것이고, 이것이 결국 정밀의료 및 의료서비스 개선사업에 개인이 자발적으로 참여할 수 있는 원동력으로 작용하는 것이다.Digital healthcare, which collects, analyzes and utilizes various generated data to provide customized medical services, is emerging as a core industry in the 4th Industrial Revolution. In order to develop healthcare services, it is necessary to collect and analyze not only medical information but also genetic information from birth or information generated in every process of daily life. The promotion of the medical industry through the use of data will not only lead to economic development, but also contribute to the promotion of people s health and the realization of human dignity and values, which are the highest goals and values of the constitutional order. Health care services based on the use of medical data will help prevent the disease and provide precise medical care and further protect the health of the people, such as shortening the time for drug development. On the other hand, the progress of science and technology has accompanied industrial development, which has led to an increase in wealth in economic terms. This has a profound effect on state operations and people s lives, including the quality of life and the efficiency of administration. Accordingly, the use of large amounts of data is essential for the development of new industries. Countries around the world, such as the United States, Japan, Germany, and Finland, have enacted separate laws for the use of medical data and are seeking ways to safely use medical data, which is sensitive information. However, medical data is collected due to the sensitivity and professionalism of the information. Processing. Special handling and management of the application is necessary. Privacy and the use of data are both invaluable values. Complementary laws and institutional arrangements should reduce the risk of data misuse and abuse, thereby ensuring the public s trust. The establishment of laws and systems for the protection of personal information is not an obstacle to the use of information, but rather a building of trust in the safe use of information. It acts as a driving force.

A Bayesian Method for the Detection of Proof of Concept in Early Phase Oncology Studies with a Basket Design

In the clinical drug development, proof of clinical concept (PoC) refers to the evidence of treatment efficacy that is obtained from early phase clinical studies. PoC is critical, as it motivates the initiation of late stage clinical trials, and has a profound impact on the “Chemistry, Manufacturing and Controls” (CMC) process, which is preferably launched as early as possible so as to save valuable time for drug development. A new type of oncology clinical trial called basket trial has emerged recently, where the experimental treatment targets on a specific oncogenic pathway that is hypothesized to modulate tumor growth and/or metastasis, and patients with potentially multiple cancer types can be enrolled. The problem of PoC in basket trials has not been formally investigated in the statistical literature. In early phase basket trials, the commonly used independent analysis lacks statistical power of detecting PoC due to limited sample size. A more powerful approach is needed, especially when the treatment effect is not strong enough for each individual cancer type. In this paper, we propose a novel approach for PoC detection in the early phase basket trials under a Bayesian framework. We classify cancer types into a “sensitive subgroup” that responds positively to the treatment, and an “insensitive subgroup” that does not respond to the treatment. We then assess PoC using the posterior probability that at least one cancer type is sensitive. Simulation results show that our proposed approach has a promising performance, with considerable gain in power compared with the independent approach when a relatively large number of the cancer types are sensitive to the treatment.

Drug repositioning or target repositioning: A structural perspective of drug-target-indication relationship for available repurposed drugs

Drug repositioning aims to find new indications for existing drugs in order to reduce drug development cost and time. Currently,there are numerous stories of successful drug repositioning that have been reported and many repurposed drugs are already available on the market. Although drug repositioning is often a product of serendipity, repositioning opportunities can be uncovered systematically. There are three systematic approaches to drug repositioning: disease-centric approach, target-centric and drug-centric. Disease-centric approaches identify close relationships between an old and a new indication. A target-centric approach links a known target and its established drug to a new indication. Lastly, a drug-centric approach connects a known drug to a new target and its associated indication. These three approaches differ in their potential and their limitations, but above all else, in the required start information and computing power. This raises the question of which approach prevails in current drug discovery and what that implies for future developments. To address this question, we systematically evaluated over 100 drugs, 200 target structures and over 300 indications from the Drug Repositioning Database. Each analyzed case was classified as one of the three repositioning approaches. For the majority of cases (more than 60%) the disease-centric definition was assigned. Almost 30% of the cases were classified as target-centric and less than 10% as drug-centric approaches. We concluded that, despite the use of umbrella term “drug” repositioning, disease- and target-centric approaches have dominated the field until now. We propose the use of drug-centric approaches while discussing reasons, such as structure-based repositioning techniques, to exploit the full potential of drug-target-disease connections.

Empirical Size and Power of a Hybrid Statistic for Matched and Unmatched Designs in Interim Analysis of Clinical Trials: A Simulation Study

Introduction In clinical trials, an interim analysis is commonly applied to obtain an evidence of a significant difference of the study interference. It could save patient resources and shorten the drug development and approval time if a trial is terminated with high confidence at an earlier time than planned. Group sequential

Potential clinical drugs as covalent inhibitors of the priming proteases of the spike protein of SARS-CoV-2

In less than eight months, the COVID-19 (coronavirus disease 2019) caused by the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus has resulted in over 20,000,000 confirmed cases and over 700,000 deaths around the world. With the increasing worldwide spreading of this disease, the lack of effective drugs against SARS-CoV-2 infection makes the situation even more dangerous and unpredictable. Although many forces are speeding up to develop prevention and treatment therapeutics, it is unlikely that any de novo drugs will be available in months. Drug repurposing holds the promise to significantly save the time for drug development, since it could use existing clinic drugs to treat new diseases. Based on the “steric-clashes alleviating receptor (SCAR)” strategy developed in our lab recently, we screened the library of clinic and investigational drugs, and identified nine drugs that might be repurposed as covalent inhibitors of the priming proteases (cathepsin B, cathepsin L, and TMPRSS2) of the spike protein of SARS-CoV-2. Among these hits, five are known covalent inhibitors, and one is an anti-virus drug. Therefore, we hope our work would provide rational and timely help for developing anti-SARS-CoV-2 drugs.

Rationalization of a traditional liver medicine using systems biology approach and its evaluation in preclinical trial

‘Bottom-up’, i.e., molecule to medicine strategy for the discovery of new drugs takes enormous time and cost. In most of the cases, inherent toxicity and undesired side effects of the developed drug hinder its way beyond the early stages of development. In this regard, the systems pharmacology can play an excellent role by reducing the cost and time of drug development through rationalization and/or repurposing of traditional drugs with known side effects. In the present study, our aim was to develop an integrated systems biology method for the prediction of active ingredients of a traditional medicine and their potential targets inside the body. Further, we evaluated the predictive capacity of the developed method in a preclinical animal model.Here, we have prepared a formulation (SKP17LIV01) from an extract of eight medicinal plants traditionally used as liver medicine and identified the constituents using UHPLC-MS technique. Using systems biology approach, we have rationalized the components of the formulation for potential use in the treatment of heavy metal-induced hepatotoxicity. The active ingredients and potential therapeutic targets were also predicted. A detailed biochemical, histopathological and molecular study on the mice model of lead toxicity confirms the efficacy of the formulation as per prediction by the systems pharmacology approach. The study may open a new frontier for re-discovery of drugs that are already used in traditional medicine.

Strategy for achieving standardized bone models.

Reliably producing functional in vitro organ models, such as organ‐on‐chip systems, has the potential to considerably advance biology research, drug development time, and resource efficiency. However, despite the ongoing major progress in the field, three‐dimensional bone tissue models remain elusive. In this review, we specifically investigate the control of perfusion flow effects as the missing link between isolated culture systems and scientifically exploitable bone models and propose a roadmap toward this goal.

The use of validated and nonvalidated surrogate endpoints in two European Medicines Agency expedited approval pathways: A cross-sectional study of products authorised 2011-2018.

BackgroundIn situations of unmet medical need or in the interests of public health, expedited approval pathways, including conditional marketing authorisation (CMA) and accelerated assessment (AA), speed up European Medicines Agency (EMA) marketing authorisation recommendations for medicinal products. CMAs are based on incomplete benefit-risk assessment data and authorisation remains conditional until regulator-imposed confirmatory postmarketing measures are fulfilled. For products undergoing AA, complete safety and efficacy data should be available, and postauthorisation measures may include only standard requirements of risk management and pharmacovigilance plans. In the pivotal trials supporting products assessed by expedited pathways, surrogate endpoints reduce drug development time compared with waiting for the intended clinical outcomes. Whether surrogate endpoints supporting products authorised through CMA and AA pathways reliably predict clinical benefits of therapy has not been studied systematically. Our objectives were to determine the extent to which surrogate endpoints are used and to assess whether their validity had been confirmed according to published hierarchies.Methods and findingsWe used European Public Assessment Reports (EPARs) to identify the primary endpoints in the pivotal trials supporting products authorised through CMA or AA pathways during January 1, 2011 to December 31, 2018. We excluded products that were vaccines, topical, reversal, or bleeding prophylactic agents or withdrawn within the study time frame. Where pivotal trials reported surrogate endpoints, we conducted PubMed searches for evidence of validity for predicting clinical outcomes. We used 2 published hierarchies to assess validity level. Surrogates with randomised controlled trials supporting the surrogate-clinical outcome relationship were rated as ‘validated’. Fifty-one products met the inclusion criteria; 26 underwent CMAs, and 25 underwent AAs. Overall, 26 products were for oncology indications, 10 for infections, 8 for genetic disorders, and 7 for other systems disorders. Five products (10%), all AAs, were authorised based on pivotal trials reporting clinical outcomes, and 46 (90%) were authorised based on surrogate endpoints. No studies were identified that validated the surrogate endpoints. Among a total of 49 products with surrogate endpoints reported, most were rated according to the published hierarchies as being ‘reasonably likely’ (n = 30; 61%) or of having ‘biological plausibility’ (n = 46; 94%) to predict clinical outcomes. EPARs did not consistently explain the nature of the pivotal trial endpoints supporting authorisations, whether surrogate endpoints were validated or not, or describe the endpoints to be reported in the confirmatory postmarketing studies. Our study has limitations: we may have overlooked relevant validation studies; the findings apply to 2 expedited pathways and may not be generalisable to products authorised through the standard assessment pathway.ConclusionsThe pivotal trial evidence supporting marketing authorisations for products granted CMA or AA was based dominantly on nonvalidated surrogate endpoints. EPARs and summary product characteristic documents, including patient information leaflets, need to state consistently the nature and limitations of endpoints in pivotal trials supporting expedited authorisations so that prescribers and patients appreciate shortcomings in the evidence about actual clinical benefit. For products supported by nonvalidated surrogate endpoints, postauthorisation measures to confirm clinical benefit need to be imposed by the regulator on the marketing authorisation holders.

A Bayesian Sequential Design for Clinical Trials With Time-to-Event Outcomes

There is increasing interest in Bayesian group sequential design because of its potential to improve efficiency in clinical trials, to shorten drug development time, and to enhance statistical inference precision without undermining the clinical trial’s integrity or validity. We propose a Bayesian sequential design for clinical trials with time-to-event outcomes and use alpha spending functions to control the overall Type I error rate. Bayes factor is adapted for decision-making at interim analyses. Algorithms are presented to make decision rules and to calculate power of the proposed tests. Sensitivity analysis is implemented to evaluate the impact of different choices of prior parameters on choosing critical values. The power of tests, the expected event size of the proposed design, and the quality of estimators are studied through simulations, and compared with the frequentist group sequential design. Simulations show that at fixed total number of events, the proposed design can achieve greater power and require smaller expected event size when appropriate priors are chosen, compared with the frequentist group sequential design. The feasibility of the proposed design is further illustrated on a real dataset. Supplementary materials for this article are available online.

Abstract 4795: Oxidative stress by proguanil suppresses breast tumor growth

Abstract Breast cancer is one of the most common malignancies and the second leading cause of cancer related mortality among women in U.S., thus developing new strategies to control breast cancer is an important mission. Repurposing of old drugs as new anti-cancer drugs is important as it can save time and cost of drug development. Proguanil is used in combination with atovaquone for the treatment of malaria. In this study, we determined the anticancer effects of proguanil in breast cancer cells. Proguanil significantly reduced the viability of MDA-MB-231, HCC1806 and MCF-7 breast cancer cells with IC50 of 42μM, 44μM and 40μM respectively after 72 h of treatment and induced apoptosis as exhibited by FITC/Annexin assay and cleavage of caspase 3 as well as PARP. Proguanil also reduced the survival of several patient-derived cells with IC50 in the range of 30-40μM. The anti-cancer effects of proguanil were associated with the generation of ROS and persistent disruption of mitochondrial membrane potential. ROS generation by proguanil was about 2-3 fold more as compared to control in MDA-MB-231 and HCC1806 cells. The generation of ROS was inhibited when the cells were pretreated with a general antioxidant N-acetyl cysteine (NAC). Moreover, exposure of MDA-MB-231 and HCC1806 cells to proguanil was also associated with increased expression of Bax, p-H2AX, c-caspase9 and down-regulation of bcl-2 and survivin. We further evaluated the oxygen consumption rate with proguanil treatment in breast cancer cells using flux analyzer. Our results showed significant decrease in the mitochondrial respiration rate and ATP production rate after proguanil treatment. Furthermore, 20mg/kg proguanil when given orally suppressed the growth of 4T1 breast tumors in female Balb/c mice by 55%. Tumors from proguanil treated mice demonstrated increased apoptosis, which was related to the increased expression of p-H2AX and Bax. Taken together, these results show that proguanil is an effective inhibitor of in vitro and in-vivo growth of breast cancer cells. These findings provide the rationale for further clinical investigation of proguanil against breast cancer. Citation Format: Nehal Gupta, Sanjay Srivastava. Oxidative stress by proguanil suppresses breast tumor growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4795.

Phase II Trials in Drug Development and Adaptive Trial Design

SummaryPhase II clinical studies represent a critical point in determining drug costs, and phase II is a poor predictor of drug success: >30% of drugs entering phase II studies fail to progress, and >58% of drugs go on to fail in phase III. Adaptive clinical trial design has been proposed as a way to reduce the costs of phase II testing by providing earlier determination of futility and prediction of phase III success, reducing overall phase II and III trial sizes, and shortening overall drug development time. This review examines issues in phase II testing and adaptive trial design.

Decision Science for Generic Drug Development and Review.

Decision Science for Generic Drug Development and Review.