Food and drug administration's critical path initiative and innovations in drug development paradigm: Challenges, progress, and controversies

Soft sensor for viable cell counting by measuring dynamic oxygen uptake rate

Regulatory authorities in biopharmaceutical industry emphasize process design by process understanding but applicable tools that are easy to implement are still missing. Soft sensors are a promising tool for the implementation of the Quality by Design (QbD) approach and Process Analytical Technology (PAT). In particular, the correlation between viable cell counting and oxygen consumption was investigated, but problems remained: Either the process had to be modified for excluding CO2 in pH control, or complex kLa models had to be set up for specific processes. In this work, a non-invasive soft sensor for simplified on-line cell counting based on dynamic oxygen uptake rate was developed with no need of special equipment. The dynamic oxygen uptake rates were determined by automated and periodic interruptions of gas supply in DASGIP® bioreactor systems, realized by a programmed Visual Basic script in the DASware® control software. With off-line cell counting, the two parameters were correlated based on linear regression and led to a robust model with a correlation coefficient of 0.92. Avoidance of oxygen starvation was achieved by gas flow reactivation at a certain minimum dissolved oxygen concentration. The soft sensor model was established in the exponential growth phase of a Chinese Hamster Ovary fed-batch process. Control studies showed no impact on cell growth by the discontinuous gas supply. This soft sensor is the first to be presented that does not require any specialized additional equipment as the methodology relies solely on the direct measurement of oxygen consumed by the cells in the bioreactor.

DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing

The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein-ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein-ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the baseline methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein-ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein-ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.

Visualizing Extracellular Vesicles and Their Function in 3D Tumor Microenvironment Models

Extracellular vesicles (EVs) are cell-derived nanostructures that mediate intercellular communication by delivering complex signals in normal tissues and cancer. The cellular coordination required for tumor development and maintenance is mediated, in part, through EV transport of molecular cargo to resident and distant cells. Most studies on EV-mediated signaling have been performed in two-dimensional (2D) monolayer cell cultures, largely because of their simplicity and high-throughput screening capacity. Three-dimensional (3D) cell cultures can be used to study cell-to-cell and cell-to-matrix interactions, enabling the study of EV-mediated cellular communication. 3D cultures may best model the role of EVs in formation of the tumor microenvironment (TME) and cancer cell-stromal interactions that sustain tumor growth. In this review, we discuss EV biology in 3D culture correlates of the TME. This includes EV communication between cell types of the TME, differences in EV biogenesis and signaling associated with differing scaffold choices and in scaffold-free 3D cultures and cultivation of the premetastatic niche. An understanding of EV biogenesis and signaling within a 3D TME will improve culture correlates of oncogenesis, enable molecular control of the TME and aid development of drug delivery tools based on EV-mediated signaling.

Entering the era of computationally driven drug development

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure-activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.

Secretory Leukocyte Protease Inhibitor (SLPI)-A Novel Predictive Biomarker of Acute Kidney Injury after Cardiac Surgery: A Prospective Observational Study

Acute kidney injury (AKI) is one of the most frequent complications after cardiac surgery and is associated with poor outcomes. Biomarkers of AKI are crucial for the early diagnosis of this condition. Secretory leukocyte protease inhibitor (SLPI) is an alarm anti-protease that has been implicated in the pathogenesis of AKI but has not yet been studied as a diagnostic biomarker of AKI. Using two independent cohorts (development cohort (DC), n = 60; validation cohort (VC), n = 148), we investigated the performance of SLPI as a diagnostic marker of AKI after cardiac surgery. Serum and urinary levels of SLPI were quantified by ELISA. SLPI was significantly elevated in AKI patients compared with non-AKI patients (6 h, DC: 102.1 vs. 64.9 ng/mL, p < 0.001). The area under the receiver operating characteristic curve of serum SLPI 6 h after surgery was 0.87 ((0.76–0.97); DC). The addition of SLPI to standard clinical predictors significantly improved the predictive accuracy of AKI (24 h, VC: odds ratio (OR) = 3.91 (1.44–12.13)). In a subgroup, the increase in serum SLPI was evident before AKI was diagnosed on the basis of serum creatinine or urine output (24 h, VC: OR = 4.89 (1.54–19.92)). In this study, SLPI was identified as a novel candidate biomarker for the early diagnosis of AKI after cardiac surgery.

Mass Spectrometry-Based Biomarkers in Drug Development

Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increases the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.

Integrating dose estimation into a decision-making framework for model-based drug development

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

Advanced Research and Data Methods in Women's Health: Big Data Analytics, Adaptive Studies, and the Road Ahead

Technical advances in science have had broad implications in reproductive and women's health care. Recent innovations in population-level data collection and storage have made available an unprecedented amount of data for analysis while computational technology has evolved to permit processing of data previously thought too dense to study. "Big data" is a term used to describe data that are a combination of dramatically greater volume, complexity, and scale. The number of variables in typical big data research can readily be in the thousands, challenging the limits of traditional research methodologies. Regardless of what it is called, advanced data methods, predictive analytics, or big data, this unprecedented revolution in scientific exploration has the potential to dramatically assist research in obstetrics and gynecology broadly across subject matter. Before implementation of big data research methodologies, however, potential researchers and reviewers should be aware of strengths, strategies, study design methods, and potential pitfalls. Examination of big data research examples contained in this article provides insight into the potential and the limitations of this data science revolution and practical pathways for its useful implementation.

Attitudes and opinions regarding confirmatory adaptive clinical trials: a mixed methods analysis from the Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT) project

Background

Adaptive designs have been increasingly used in the pharmaceutical and device industries, but adoption within the academic setting has been less widespread — particularly for confirmatory phase trials. We sought to understand perceptions about understanding, acceptability, and scientific validity of adaptive clinical trials (ACTs).

Methods

We used a convergent mixed methods design using survey and mini-focus group data collection procedures to elucidate attitudes and opinions among “trial community” stakeholders regarding understanding, acceptability, efficiency, scientific validity, and speed of discovery with adaptive designs. Data were collected about various aspects of ACTs using self-administered surveys (paper or Web-based) with visual analog scales (VASs) with free text responses and with mini-focus groups of key stakeholders. Participants were recruited as part of an ongoing NIH/FDA-funded research project exploring the incorporation of ACTs into an existing NIH network that focuses on confirmatory phase clinical trials in neurological emergencies. “Trial community” representatives, namely, clinical investigators, biostatisticians, NIH officials, and FDA scientists involved in the planning of four clinical trials, were eligible to participate. In addition, recent and current members of a clinical trial-oriented NIH study section were also eligible.

Results

A total of 76 stakeholders completed the survey (out of 91 who were offered it, response rate 84 %). While the VAS attitudinal data showed substantial variability across respondents about acceptability and understanding of ACTs by various constituencies, respondents perceived clinicians to be less likely to understand ACTs and that ACTs probably would increase the efficiency of discovery. Textual and focus group responses emerged into several themes that enhanced understanding of VAS attitudinal data including the following: acceptability of adaptive designs depends on constituency and situation; there is variable understanding of ACTs (limited among clinicians, perceived to be higher at FDA); views about the potential for efficiency depend on the situation and implementation. Participants also frequently mentioned a need for greater education within the academic community. Finally, the empiric, non-quantitative selection of treatments for phase III trials based on limited phase II trials was highlighted as an opportunity for improvement and a potential explanation for the high number of neutral confirmatory trials.

Conclusions

These data show considerable variations in attitudes and beliefs about ACTs among trial community representatives. For adaptive trials to be fully considered when appropriate and for the research enterprise to realize the full potential of adaptive designs will likely require extensive experience and trust building within the trial community.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-016-1493-z) contains supplementary material, which is available to authorized users.

Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease

BACKGROUND & AIMS

Multiparametric magnetic resonance (MR) imaging has been demonstrated to quantify hepatic fibrosis, iron, and steatosis. The aim of this study was to determine if MR can be used to predict negative clinical outcomes in liver disease patients.

METHODS

Patients with chronic liver disease (n=112) were recruited for MR imaging and data on the development of liver related clinical events were collected by medical records review. The median follow-up was 27months. MR data were analysed blinded for the Liver Inflammation and Fibrosis score (LIF; <1, 1-1.99, 2-2.99, and ⩾3 representing normal, mild, moderate, and severe liver disease, respectively), T2∗ for liver iron content and proportion of liver fat. Baseline liver biopsy was performed in 102 patients.

RESULTS

Liver disease aetiologies included non-alcoholic fatty liver disease (35%) and chronic viral hepatitis (30%). Histologically, fibrosis was mild in 54 (48%), moderate in 17 (15%), and severe in 31 (28%) patients. Overall mortality was 5%. Ten patients (11%) developed at least one liver related clinical event. The negative predictive value of LIF<2 was 100%. Two patients with LIF 2-2.99 and eight with LIF⩾3 had a clinical event. Patients with LIF⩾3 had a higher cumulative risk for developing clinical events, compared to those with LIF<1 (p=0.02) and LIF 1-1.99 (p=0.03). Cox regression analysis including all 3 variables (fat, iron, LIF) resulted in an enhanced LIF predictive value.

CONCLUSIONS

Non-invasive standardised multiparametric MR technology may be used to predict clinical outcomes in patients with chronic liver disease.

Strategies and endpoints of antifibrotic drug trials: Summary and recommendations from the AASLD Emerging Trends Conference, Chicago, June 2014

There is an urgent need to develop antifibrotic therapies for chronic liver disease, and clarify which endpoints in antifibrotic trials will be acceptable to regulatory agencies. The American Association for the Study of Liver Diseases sponsored an endpoints conference to help accelerate the efficient testing of antifibrotic agents and develop recommendations on clinical trial design for liver fibrosis. In this review, we summarize the salient and novel elements of this conference and provide directions for future clinical trial design. The article follows the structure of the conference and is organized into five areas: (1) antifibrotic trial design; (2) preclinical proof-of-concept studies; (3) pharmacological targets, including rationale and lessons to learn; (4) rational drug design and development; and (5) consensus and recommendations on design of clinical trials in liver fibrosis. Expert overviews and collaborative discussions helped to summarize the key unmet needs and directions for the future, including: (1) greater clarification of at-risk populations and study groups; (2) standardization of all elements of drug discovery and testing; (3) standardization of clinical trial approaches; (4) accelerated development of improved noninvasive markers; and (5) need for exploration of potential off-target toxicities of future antifibrotic drugs.

An implantable microdevice to perform high-throughput in vivo drug sensitivity testing in tumors

Current anticancer chemotherapy relies on a limited set of in vitro or indirect prognostic markers of tumor response to available drugs. A more accurate analysis of drug sensitivity would involve studying tumor response in vivo. To this end, we have developed an implantable device that can perform drug sensitivity testing of several anticancer agents simultaneously inside the living tumor. The device contained reservoirs that released microdoses of single agents or drug combinations into spatially distinct regions of the tumor. The local drug concentrations were chosen to be representative of concentrations achieved during systemic treatment. Local efficacy and drug concentration profiles were evaluated for each drug or drug combination on the device, and the local efficacy was confirmed to be a predictor of systemic efficacy in vivo for multiple drugs and tumor models. Currently, up to 16 individual drugs or combinations can be assessed independently, without systemic drug exposure, through minimally invasive biopsy of a small region of a single tumor. This assay takes into consideration physiologic effects that contribute to drug response by allowing drugs to interact with the living tumor in its native microenvironment. Because these effects are crucial to predicting drug response, we envision that these devices will help identify optimal drug therapy before systemic treatment is initiated and could improve drug response prediction beyond the biomarkers and in vitro and ex vivo studies used today. These devices may also be used in clinical drug development to safely gather efficacy data on new compounds before pharmacological optimization.

Emerging approaches in predictive toxicology

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described.

Practically convenient and industrially-aligned methods for iridium-catalysed hydrogen isotope exchange processes

The use of alternative solvents in the iridium-catalysed hydrogen isotope exchange reaction with developing phosphine/NHC Ir(I) complexes has identified reaction media which are more widely applicable and industrially acceptable than the commonly employed chlorinated solvent, dichloromethane. Deuterium incorporation into a variety of substrates has proceeded to deliver high levels of labelling (and regioselectivity) in the presence of low catalyst loadings and over short reaction times. The preparative outputs have been complemented by DFT studies to explore ligand orientation, as well as solvent and substrate binding energies within the catalyst system.

Mass spectrometry-based biomarkers in drug development

Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance, and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement, and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increase the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.

Data Mining and Meta-Analysis on DNA Microarray Data

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

Mechanistic systems modeling to guide drug discovery and development

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research.

Preclinical in vivo ADME studies in drug development: a critical review

INTRODUCTION

The last two decades have brought many fundamental changes to the drug development process. One such change is the importance of preclinical pharmacokinetics, which has become an essential part of early drug discovery. Furthermore, bioanalytical methods have become more sensitive and the identification and quantitation of metabolites can now be carried out on limited amount of biological material. There has also been a change in regulatory expectations, which are now particularly focused on the safety of human metabolites.

AREAS COVERED

The focus of this paper is on some 'traditional' in vivo ADME studies: excretion balance, metabolic profile and WBA in the toxicological species. These studies, performed with radiolabeled material, have a long history: and are a regular presence in submission dossiers. This paper reviews their value in the perspective of the contemporary drug development process.

EXPERT OPINION

These experiments may sometimes still be relevant to explain toxicological findings or for other special purposes but should not be considered required pieces of the registration dossiers. An appropriate investigation of samples coming from safety evaluation and human Phase I studies and the knowledge generated during the lead optimization phase provide, in most instances, all the DMPK information needed to take decisions in the drug development process.

Bayesian adaptive clinical trials: a dream for statisticians only?

Adaptive or 'flexible' designs have emerged, mostly within frequentist frameworks, as an effective way to speed up the therapeutic evaluation process. Because of their flexibility, Bayesian methods have also been proposed for Phase I through Phase III adaptive trials; however, it has been reported that they are poorly used in practice. We aim to describe the international scientific production of Bayesian clinical trials by investigating the actual development and use of Bayesian 'adaptive' methods in the setting of clinical trials. A bibliometric study was conducted using the PubMed and Science Citation Index-Expanded databases. Most of the references found were biostatistical papers from various teams around the world. Most of the authors were from the US, and a large proportion was from the MD Anderson Cancer Center (University of Texas, Houston, TX). The spread and use of these articles depended heavily on their topic, with 3.1% of the biostatistical articles accumulating at least 25 citations within 5 years of their publication compared with 15% of the reviews and 32% of the clinical articles. We also examined the reasons for the limited use of Bayesian adaptive design methods in clinical trials and the areas of current and future research to address these challenges. Efforts to promote Bayesian approaches among statisticians and clinicians appear necessary.

Medication errors--new approaches to prevention

Medication errors in pediatric anesthesia represent an important risk to children. Concerted action to reduce harm from this cause is overdue. An understanding of the genesis of avoidable adverse drug events may facilitate the development of effective countermeasures to the events or their effects. Errors include those involving the automatic system of cognition and those involving the reflective system. Errors and violations are distinct, but violations often predispose to error. The system of medication administration is complex, and many aspects of it are conducive to error. Evidence-based practices to reduce the risk of medication error in general include those encompassed by the following recommendations: systematic countermeasures should be used to decrease the number of drug administration errors in anesthesia; the label on any drug ampoule or syringe should be read carefully before a drug is drawn up or injected; the legibility and contents of labels on ampoules and syringes should be optimized according to agreed standards; syringes should always be labeled; formal organization of drug drawers and workspaces should be used; labels should be checked with a second person or a device before a drug is drawn up or administered. Dosage errors are particularly common in pediatric patients. Causes that should be addressed include a lack of pediatric formulations and/or presentations of medication that necessitates dilution before administration or the use of intravenous formulations for oral administration in children, a frequent failure to obtain accurate weights for patients and a paucity of pharmacokinetic and pharmacodynamic data. Technological innovations, including the use of bar codes and various cognitive aids, may facilitate compliance with these recommendations. Improved medication safety requires a system-wide strategy standardized at least to the level of the institution; it is the responsibility of institutional leadership to introduce such strategies and of individual practitioners to engage in them.