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There have been remarkable advances in the early identification of behavioral changes predictive of neurotoxicity induced by xenobiotic administrations in laboratory animals during Tier I safety testing. By definition, this risk assessment is conducted before formal approval for use in humans. The intent of this regulatory-based drug approval process is to ensure public health, domestic tranquility and provide for the common defense against unwitting exposure to harmful therapeutics and promote the general welfare of human patients seeking the diagnosis, cure, mitigation, treatment, or prevention of disease. There is a contemporary movement by many researchers to diminish the threat to humans for many known toxicants to nullify, diminish and obviate the great advances in drug safety that have been established as the foundation of ‘good science’ in the drug approval process. The biggest threat to contemporary neurotoxicity screening may be emanating from inside the laboratory.

The Food and Drug Administration (FDA) classifies herbal preparations as food supplements. New herbal supplements and products are not governed by the strict FDA drug approval process and there is no premarket approval required. The FDA prohibits manufacturers and distributors from marketing adulterated or misbranded products but does not rigorously define safe practices. Scientific evidence related to herbal supplements is limited. Herbal supplements have been associated with adverse reactions and herbal-drug interactions. Information and precautions for 20 common herbal supplements, including St. John's wort, ginseng, echinacea, and ginkgo, are reviewed. Resources for consumers and health care professionals are highlighted.

Introduction Tofacitinib is an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis (RA). To provide additional clinical evidence in regulatory submissions for a modified-release (MR) once-daily (QD) tofacitinib formulation, we compared real-world adherence and effectiveness between patients initiating the MR QD formulation and patients initiating an immediate-release (IR) twice-daily (BID) formulation. Methods Two noninterventional cohort studies were conducted. First, adherence and two effectiveness proxies were compared between patients with RA who newly initiated tofacitinib MR 11 mg QD or IR 5 mg BID in the IBM® MarketScan® Commercial and Medicare Supplemental US insurance claims databases (March 2016–October 2018). Second, using data collected in the Corrona US RA Registry (February 2016–August 2019), two Clinical Disease Activity Index (CDAI)-based measures of effectiveness were compared between tofacitinib MR 11 mg QD and IR 5 mg BID, and against noninferiority criteria derived from placebo-controlled clinical trials of the tofacitinib IR formulation. Multiple sensitivity analyses of the registry data were conducted to reassure regulators of consistent results across different assumptions. Results In each study, approximately two-thirds of patients initiated the MR formulation. In the claims database study, improved adherence and at least comparable effectiveness were observed with tofacitinib MR vs IR over 12 months, particularly in patients without prior advanced therapy. In the registry study, the noninferiority of tofacitinib MR vs IR was demonstrated for both CDAI outcomes at ~6 months; this finding was robust across multiple sensitivity analyses. Conclusion These results demonstrate the value of real-world evidence from complementary data sources in understanding the impact of medication adherence with a QD formulation in clinical practice. These analyses were suitable for regulatory consideration as an important component of evidence for the comparability of tofacitinib MR 11 mg QD vs IR 5 mg BID in patients with RA. Trial Registration Claims database study: ClinicalTrials.gov identifier NCT04018001, retrospectively registered July 12, 2019. Corrona US RA Registry study: ClinicalTrials.gov identifier NCT04267380, retrospectively registered February 12, 2020. Electronic supplementary material The online version of this article (10.1007/s12325-020-01501-z) contains supplementary material, which is available to authorized users.

New therapeutic compounds go through a preclinical drug cardiotoxicity screening process that is overly conservative and provides limited mechanistic insight, leading to the misclassification of potentially beneficial drugs as proarrhythmic. There is a need to develop a screening paradigm that maintains this high sensitivity, while ensuring non-cardiotoxic compounds pass this phase of the drug approval process. In this study, we develop an in vitro-in silico pipeline using human induced stem-cell derived cardiomyocytes (iPSC-CMs) to address this problem. The pipeline includes a model-guided optimization that produces a voltage-clamp (VC) protocol to determine drug block of seven cardiac ion channels. Such VC data, along with action potential (AP) recordings, were acquired from iPSC-CMs before and after treatment with a control solution or a low-, intermediate-, or high-risk drug. We identified significant AP prolongation (a proarrhythmia indicator) in two high-risk drugs and, from the VC data, determined strong ion channel blocks that led to the AP changes. The VC data also uncovered an undocumented funny current (If) block by quinine, which we confirmed with experiments using a HEK-293 expression line. We present a new approach to cardiotoxicity screening that simultaneously evaluates proarrhythmia risk (e.g. AP prolongation) and mechanism (e.g. channel block) from iPSC-CMs.

Before placing a drug on the market for human use, it must be comprehensively studied in preclinical and clinical studies to ensure it is safe, high-quality and effective to be used in the target population. Nevertheless, there are not enough studies carried out for the pediatric population. Many of the medications currently used to treat the pediatric population have not been authorized for such use. Problems resulting from inadequate adaptation of adult medications for children include inadequate dosing which leads to increased risk of adverse reactions including death, ineffective treatment, non-availability to the pediatric population of therapeutic advances, non-suitable formulations, lack of formulations and inadequate administration routes, as well as use of magistral or officinal formulations to treat the pediatric population which may be of poor quality. The development of pediatric medicines will lead to safe and accurate administration, reducing the risk of medication errors, improving adherence to medication and improving therapeutic outcomes in children. For these reasons, it is necessary that the regulatory authorities in each country encourage the research and development of pediatric medicines. The objective of this article was reviewing the pharmacological and regulatory aspects involved in the development of pediatric medications, as well as describing the current status of pediatric drug development in Mexico.

Implicit in the drug-approval process is a host of decisions -- target patient population, control group, primary endpoint, sample size, follow-up period -- all of which determine the trade-off between Type I and Type II error. We explore the application of Bayesian decision analysis (BDA) to minimize the expected cost of drug approval, where the relative costs of the two types of errors are calibrated using U.S. Burden of Disease Study 2010 data. The results for conventional fixed-sample randomized clinical-trial designs suggest that for terminal illnesses with no existing therapies such as pancreatic cancer, the standard threshold of 2.5% is substantially more conservative than the BDA-optimal threshold of 23.9% to 27.8%. For relatively less deadly conditions such as prostate cancer, 2.5% is more risk-tolerant or aggressive than the BDA-optimal threshold of 1.2% to 1.5%. We compute BDA-optimal sizes for 25 of the most lethal diseases and show how a BDA-informed approval process can incorporate all stakeholders' views in a systematic, transparent, internally consistent, and repeatable manner.

Aims The European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) produce guidelines for the design of pivotal psychiatric drug trials used in new drug applications. It is unknown who are involved in the guideline development and what specific trial design recommendations they give. Methods Cross-sectional study of EMA Clinical Efficacy and Safety Guidelines and FDA Guidance Documents. Study outcomes: (1) guideline committee members and declared conflicts of interest; (2) guideline development and organisation of commenting phases; (3) categorisation of stakeholders who comment on draft and final guidelines according to conflicts of interest (‘industry’, ‘not-industry but with industry-related conflicts’, ‘independent’, ‘unclear’); and (4) trial design recommendations (trial duration, psychiatric comorbidity, ‘enriched design’, efficacy outcomes, comparator choice). Protocol registration https://doi.org/10.1101/2020.01.22.20018499 (27 January 2020). Results We included 13 EMA and five FDA guidelines covering 15 psychiatric indications. Eleven months after submission, the EMA had not processed our request regarding committee member disclosures. FDA offices draft the Guidance Documents, but the Agency is not in possession of employee conflicts of interest declarations because FDA employees generally may not hold financial interests (although some employees may hold interests up to $15,000). The EMA and FDA guideline development phases are similar; drafts and final versions are publicly announced and everybody can submit comments. Seventy stakeholders commented on ten guidelines: 38 (54%) ‘industry’, 18 (26%) ‘not-industry but with industry-related conflicts’, six (9%) ‘independent’ and eight (11%) ‘unclear’. They submitted 1014 comments: 640 (68%) ‘industry’, 243 (26%) ‘not-industry but with industry-related conflicts’, 44 (5%) ‘independent’ and 20 (2%) ‘unclear’ (67 could not be assigned to a specific stakeholder). The recommended designs were generally for trials of short duration; with restricted trial populations; allowing previous exposure to the drug; and often recommending rating scale efficacy outcomes. EMA mainly recommended three arm designs (both placebo and active comparators), whereas FDA mainly recommended placebo-controlled designs. There were also other important differences and FDA's recommendations regarding the exclusion of psychiatric comorbidity seemed less restrictive. Conclusions The EMA and FDA clinical research guidelines for psychiatric pivotal trials recommend designs that tend to have limited generalisability. Independent and non-conflicted stakeholders are underrepresented in the guideline development. It seems warranted with more active involvement of scientists and independent organisations without conflicts of interest in the guideline development process.

Drug regulatory documents published alongside the authorisation of aripiprazole intramuscular depot for schizophrenia and bipolar disorder identify clinical, methodological, and ethical issues related to the pivotal trials and the drug approval process.

BACKGROUND AND PURPOSEClinical trials provide essential evidence in the US Food and Drug Administration (FDA) drug approval process. Women were underrepresented in the enrollment of clinical trials for many medical conditions other than cerebrovascular disease (CVD). We investigated women’s participation in FDA-registered CVD-related interventional clinical trials and assessed whether women CVD patients in the United States were underrepresented. We also evaluated the association between the underrepresentation and the features of design and operation among these trials.METHODSWe systematically reviewed the phase 2 and 3 CVD-related interventional trials started in 2002–2017 in the FDA database. The proportions of women enrolled in these trials and the proportion-to-prevalence ratios (PPRs), defined as the ratios of the proportions of women enrolled in these trials to the proportion of women prevalent among US CVD patients during the same period, were calculated and summarized by a meta-analysis approach. We used boosted regression tree, a machine learning model, to identify the determinants of women’s underrepresentation in CVD-related trials.RESULTSAccording to our selection criteria, we analyzed the data of 145 CVD-related trials, among which 40.9% (95% CI: 38.3–43.5%) of the patients enrolled were women. Their PPR was estimated to be 0.843 (95% CI: 0.796–0.890). We found that four factors substantially influenced women’s underrepresentation in these trials: the number of enrollment sites, the starting year, randomization, and academic institution sponsorship.CONCLUSIONSGenerally, women with CVD in the United States were underrepresented in FDA registered trials started in 2002–2017. In addition, trials with greater numbers of enrollment sites, randomization, and non-academically sponsored trials had a higher risk of underrepresenting women with CVD in the United States. Investigators should take these factors into consideration in clinical trial design in the future, by either increasing women’s participation or stratifying the enrollment by gender.

In 2018, the National Cancer Institute (NCI) and the NRG Oncology partnered for the first time to host a joint Workshop on Systemic Radiopharmaceutical Therapy (RPT) to specifically address issues and strategies of dosimetry for future clinical trials. The workshop focused on (1) current dosimetric approaches for clinical trials, (2) strategies under development that would provide optimal dose reporting, and (3) future desired/optimized approaches for the new and novel emerging radionuclides and carriers in development. In this proceedings, we review the main approaches that are applied clinically to calculate the absorbed dose: These include absorbed doses calculated over a variety of spatial scales including organ, suborgan, and voxel, all achievable within the Medical Internal Radiation Dose (MIRD) schema (S-value) can be calculated with analytic methods or Monte Carlo methods, the latter in most circumstances. This proceeding will also contrast currently available methods and tools with those used in the past, to propose a pathway whereby dosimetry helps the field by optimizing the biological effect of the treatment and trial design in the drug approval process to reduce financial and logistical costs. We will also discuss the dosimetric equivalent of biomarkers to help bring a precision medicine approach to RPT implementation-when merited by evidence collected during early-phase trial investigations. Advances in the methodology and related tools have made dosimetry the optimum biomarker for RPT.

For a variety of reasons, most drug candidates cannot eventually pass the drug approval process. Thus, developing reliable methods for predicting clinical trial outcomes of drug candidates is crucial in improving the success rate of drug discovery and development. In this study, we propose an ensemble classifier based on weighted least squares support vector regression (LS-SVR) for predicting successes and failures of clinical trials. The efficacy of the proposed ensemble classifier is demonstrated through an experimental study on PrOCTOR dataset, which consists of informative chemical features of the drugs and target-based features. Comparing random forest and other models, the proposed ensemble classifier obtains the highest value for the area under the receiver operator curve (AUC). The results of this study demonstrate that the proposed ensemble classifier can be used to effectively predict drug approvals.

This chapter presents information on the Food and Drug Administration (FDA) acceptance criteria and steps, quality control, and safety assessment protocols. The approval of a drug by FDA generally entails that the data on the drug's effects have been reviewed by appropriate body such as the Center for Drug Evaluation and Research (CDER), which certifies that the drug provides benefits that outweighs its potential risks or not. The drug approval process takes place within a structured framework that includes analysis of the target condition and available treatments, assessment of benefits and risks from clinical data, and evaluating strategies for managing these risks. Because of the unique nature of botanical drugs, CDER developed plans and specific recommendations for botanical drugs to be submitted in new drug applications (NDAs). However, FDA has faced criticism that its processes are too long and too expensive. In this chapter we will discuss criteria and steps for acceptanceffigure of new drugs especially those of botanical origin and the problems facing this process which may include FDA postmarketing drug withdrawals because of safety issue that most probably cannot be corrected.

Classical drug design methodologies are hugely costly and time-consuming, with approximately 85% of the new proposed molecules failing in the first three phases of the FDA drug approval process. Thus, strategies to find alternative indications for already approved drugs that leverage computational methods are of crucial relevance. We previously demonstrated the efficacy of the Non-negative Matrix Tri-Factorization, a method that allows exploiting both data integration and machine learning, to infer novel indications for approved drugs. In this work, we present an innovative enhancement of the NMTF method that consists of a shortest-path evaluation of drug-protein pairs using the protein-to-protein interaction network. This approach allows inferring novel protein targets that were never considered as drug targets before, increasing the information fed to the NMTF method. Indeed, this novel advance enables the investigation of drug-centric predictions, simultaneously identifying therapeutic classes, protein targets and diseases associated with a particular drug. To test our methodology, we applied the NMTF and shortest-path enhancement methods to an outdated collection of data and compared the predictions against the most updated version, obtaining very good performance, with an Average Precision Score of 0.82. The data enhancement strategy allowed increasing the number of putative protein targets from 3,691 to 15,295, while the predictive performance of the method is slightly increased. Finally, we also validated our top-scored predictions according to the literature, finding relevant confirmation of predicted interactions between drugs and protein targets, as well as of predicted annotations between drugs and both therapeutic classes and diseases.

Recently many new approaches for repurposing or repositioning of the clinically used drugs have been developed. Drug repurposing allows not only to use known schemes for the synthesis of biologically active compounds, but also to avoid multiple studies that are necessary for drug approval process – analysis of pharmacokinetics, carcinogenicity, acute and chronic toxicity, including cardiotoxicity, nephrotoxicity, allergenicity etc. It makes possible to reduce the number of experimental studies as well as costs of investigations. In cancer research drug repurposing includes screening for medicines used nowadays for the treatment of patients with non-cancer diseases which possess anticancer activity or able to enhance the effects of the standard anticancer chemotherapy, and search for new applications of known anticancer drugs for the treatment of different cancer types. Scientific rationale for the search of the compounds with potential anticancer properties among drugs with different applications is based on the multiple cross-talks of signaling pathways, which can inhibit cell proliferation. Modern advances in genomics, proteomics and bioinformatics, development of permanently improving databases of drug molecular effects and high throughput analytical systems allow researchers to analyze simultaneously a large bulk of existing drugs and specific molecular targets. This review describes the main approaches and resources currently used for the drug repurposing, as well as a number of examples.

The drug approval process is the vehicle through which drug sponsors formally approve a new pharmaceutical for sale and marketing. The goals of the approval process are to provide enough information about the drug safety and efficacy in human beings. Every country has its own regulatory authority, which is responsible to enforce the rules and regulations and issue the guidelines to regulate the marketing of the drugs. The approval process starts with preclinical testing. For drugs that appear safe, an investigational new drug application (IND) is filed. If approved, clinical trials begin with phase 1 study that focus on safety and pharmacology. Phase 2 studies examine the effectiveness of the compound. Phase 3 is the final step before submitting a new drug application to the authority. A New Drug Application (NDA) contains all the information obtained during all phases of testing. Phase 4 studies, or post-marketing studies, are conducted after a product is approved.

The critics of the Innovator pharmaceutical industry allege that secondary patents are trivial modifications over the primary patent, which extend its term and delay the entry of the generics in the market place. The protagonists regard secondary patents a result of continuous research and development (RD), which help them introduce and protect new, differentiated products.The areas covered are Product life cycle management (PLCM), Drug approval process, Orange book (OB) listed patents, US patent data.Our analysis of the patents and products of four innovators viz., AstraZeneca, Takeda, Eisai and Wyeth in the field of proton pump inhibitors (PPI's) and Merck and Pfizer in the field of Statins shows that secondary patents help innovators sustain competition against other innovators in the specific product segment. The number of secondary patents listed in OB per NCE depends on the innovators interest in exploiting the NCE, the success of RD effort and product lifecycle management strategy in the wake of market competition. Market entry decisions of innovators are strategic rather than a mere fallout of the secondary patents granted. Entry of another innovator is more unpredictable and hurts the first entrant more vis a vis the entry of generics who can enter the market when the patents protecting a product are no more enforceable, and hence more predictable. Generic entry in the field of PPI's shows that the term of the primary patent is not extended by the secondary patents.

Background There is an active debate about the role that endpoints other than overall survival (OS) should play in the drug approval process. Yet the term ‘surrogate endpoint’ implies that OS is the only critical metric for regulatory approval of cancer treatments. We systematically analyzed the relationship between U.S. Food and Drug Administration (FDA) approval and publication of OS evidence to understand better the risks and benefits of delaying approval until OS evidence is available. Scope Using the PACE Continuous Innovation Indicators (CII) platform, we analyzed the effects of cancer type, treatment goal, and year of approval on the lag time between FDA approval and publication of first significant OS finding for 53 treatments approved between 1952 and 2016 for 10 cancer types (n = 71 approved indications). Findings Greater than 59% of treatments were approved before significant OS data for the approved indication were published. Of the drugs in the sample, 31% had lags between approval and first published OS evidence of 4 years or longer. The average number of years between approval and first OS evidence varied by cancer type and did not reliably predict the eventual amount of OS evidence accumulated. Conclusions Striking the right balance between early access and minimizing risk is a central challenge for regulators worldwide. We illustrate that endpoints other than OS have long helped to provide timely access to new medicines, including many current standards of care. We found that many critical drugs are approved many years before OS data are published, and that OS may not be the most appropriate endpoint in some treatment contexts. Our examination of approved treatments without significant OS data suggests contexts where OS may not be the most relevant endpoint and highlights the importance of using a wide variety of fit-for-purpose evidence types in the approval process.

Drug-drug interactions (DDIs) occur commonly and may lead to severe adverse drug reactions if not handled appropriately. Considerable information to support clinical decision making regarding potential DDIs is available in the literature and through various systems providing electronic decision support for healthcare providers. The challenge for the prescribing physician lies in sorting out the evidence and identifying those drugs for which potential interactions are likely to become clinically manifest. P-glycoprotein (P-gp) is a drug transporting protein that is found in the plasma membranes in cells of barrier and elimination organs, and plays a role in drug absorption and excretion. Increasingly, P-gp has been acknowledged as an important player in potential DDIs and a growing body of information on the role of this transporter in DDIs has become available from research and from the drug approval process. This has led to a clear need for a comprehensive review of P-gp-mediated DDIs with a focus on highlighting the drugs that are likely to lead to clinically relevant DDIs. The objective of this review is to provide information for identifying and interpreting evidence of P-gp-mediated DDIs and to suggest a classification for individual drugs based on both in vitro and in vivo evidence (substrates, inhibitors and inducers). Further, various ways of handling potential DDIs in clinical practice are described and exemplified in relation to drugs interfering with P-gp.

Animal healthcare is a booming market catering needs of several companion and production purpose animals. Animal drug approval process in the United States (US) involves submission of an Investigational New Animal Drug Application (INAD), followed by submission of New Animal Drug Application (NADA) to the Food and Drug Administration’s (FDA) Center for Veterinary Medicine (CVM). Although regional variations are present in the regulatory process for veterinary healthcare products across the globe, harmonization initiatives like International Cooperation on Harmonization of Technical Requirements for the Registration of Veterinary Medicinal Products (VICH) are playing a vital role in the veterinary medicinal product registration process by availing detailed guidance documents. The current article focuses on the regulatory requirements for new animal drug registration in the US.

Today’s surge of big data coming from multiple sources is raising the stakes that pharmacovigilance has to win, making evidence synthesis a more and more robust approach in the field. In this scenario, many scholars believe that new computational methods derived from data mining will effectively enhance the detection of early warning signals for adverse drug reactions, solving the gauntlets that post-marketing surveillance requires. This article highlights the need for a philosophical approach in order to fully realize a pharmacovigilance 2.0 revolution. A state of the art on evidence synthesis is presented, followed by the illustration of E-Synthesis, a Bayesian framework for causal assessment. Computational results regarding dose-response evidence are shown at the end of this article.

To evaluate some complex drugs, the Food and Drug Administration (FDA) creates committees of experts who recommend whether the drugs should be approved or rejected. The popular press and earlier studies commonly report that the FDA almost never overrules these recommendations. Some say this is because both the FDA and the experts have financial ties to drug companies, leading them to approve too many drugs. Others claim the FDA and experts are both risk averse, leading them to reject too many drugs when they fear approval could draw criticism from the press and Congress. We show that FDA overrules are fairly common (16 percent of cases), and that financial ties to drug companies do not explain them. We find that in a majority of overrules, experts recommend rejection, but the FDA approves the drug anyway, suggesting that the experts could be more risk averse than the FDA. This is consistent with our finding that committees with top experts reject more drugs, perhaps to protect their reputations from being associated with drugs that later might be judged unsafe.