Phases Of Drug Development Research Articles

Machine Learning in Drug Discovery: A Review.

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

Impact of simulated lung fluid components on the solubility of inhaled drugs and predicted in vivo performance

Orally inhaled products (OIPs) are gaining increased attention, as pulmonary delivery is a preferred route for the treatment of various diseases. Yet, the field of inhalation biopharmaceutics is still in development phase. For a successful correlation between various in vitro data obtained during formulation characterization and in vivo performance, it is necessary to understand the impact of parameters such as solubility and dissolution of drugs. In this work, we used in vitro-in silico feedback-feedforward approach to gain a better insight into the biopharmaceutics behavior of inhaled Salbutamol Sulphate (SS) and Budesonide (BUD). The thorough characterization of the in vitro test media and the impact of different in vitro fluid components such as lipids and protein on the solubility of aforementioned drugs was studied. These results were subsequently used as an input into the developed in silico models to investigate potential PK parameter changes in vivo. Results revealed that media comprising lipids and albumin were the most biorelevant and impacted the solubility of BUD the most. On the contrary, no notable impact was seen in case of SS. The use of simple media such as phosphate buffer saline (PBS) might be sufficient to use in solubility studies of the highly soluble and permeable drugs. However, its use for the poorly soluble drugs is limited due to the greater potential for interactions within in vivo environment. The use of in silico tools showed that the model response varies, depending on the used media. Therefore, this work highlights the relevance of carefully selecting the media composition when investigating solubility and dissolution behavior, especially in the early phases of drug development and of poorly soluble drugs.

Immune cell profiling in atherosclerosis: role in research and precision medicine.

Inflammation is intimately involved at all stages of atherosclerosis and remains a substantial residual cardiovascular risk factor in optimally treated patients. The proof of concept that targeting inflammation reduces cardiovascular events in patients with a history of myocardial infarction has highlighted the urgent need to identify new immunotherapies to treat patients with atherosclerotic cardiovascular disease. Importantly, emerging data from new clinical trials show that successful immunotherapies for atherosclerosis need to be tailored to the specific immune alterations in distinct groups of patients. In this Review, we discuss how single-cell technologies — such as single-cell mass cytometry, single-cell RNA sequencing and cellular indexing of transcriptomes and epitopes by sequencing — are ideal for mapping the cellular and molecular composition of human atherosclerotic plaques and how these data can aid in the discovery of new precise immunotherapies. We also argue that single-cell data from studies in humans need to be rigorously validated in relevant experimental models, including rapidly emerging single-cell CRISPR screening technologies and mouse models of atherosclerosis. Finally, we discuss the importance of implementing single-cell immune monitoring tools in early phases of drug development to aid in the precise selection of the target patient population for data-driven translation into randomized clinical trials and the successful translation of new immunotherapies into the clinic.

Identifying treatment effect heterogeneity in dose-finding trials using Bayesian hierarchical models: Identifying treatment effect heterogeneity in dose-finding trials.

An important task in drug development is to identify patients, which respond better or worse to an experimental treatment. Identifying predictive covariates, which influence the treatment effect and can be used to define subgroups of patients, is a key aspect of this task. Analyses of treatment effect heterogeneity are however known to be challenging, since the number of possible covariates or subgroups is often large, while samples sizes in earlier phases of drug development are often small. In addition, distinguishing predictive covariates from prognostic covariates, which influence the response independent of the given treatment, can often be difficult. While many approaches for these types of problems have been proposed, most of them focus on the two-arm clinical trial setting, where patients are given either the treatment or a control. In this article we consider parallel groups dose-finding trials, in which patients are administered different doses of the same treatment. To investigate treatment effect heterogeneity in this setting we propose a Bayesian hierarchical dose-response model with covariate effects on dose-response parameters. We make use of shrinkage priors to prevent overfitting, which can easily occur, when the number of considered covariates is large and sample sizes are small. We compare several such priors in simulations and also investigate dependent modeling of prognostic and predictive effects to better distinguish these two types of effects. We illustrate the use of our proposed approach using a Phase II dose-finding trial and show how it can be used to identify predictive covariates and subgroups of patients with increased treatment effects.

SP2-3 The impact of the COVID-19 global pandemic on oncology drug development, clinical research and R&D in Japan

The challenges due to the impact of the coronavirus disease 2019 (COVID-19) pandemic and the unprecedented public health crisis are severely affecting oncology clinical trials, early to late phase drug development and clinical research. There has been a decrease in the numbers of new trial initiations, site activations, and patient enrollments, as well as negative impacts upon on-site monitoring, protocol compliance/deviations, and safety assurance for patients with cancer participating in oncology clinical trials.

Real-world evidence in hepatocellular carcinoma.

Real-world evidence includes all health-related information, such as electronic health records, insurance claims, pharmacy records and wearables that are obtained outside of clinical trials. These data can provide critical insights into the natural history of disease and evaluate the safety and effectiveness of treatment regimens used in clinical practice. Real-world data have been applied to varying degrees by global regulatory agencies to inform and expedite many phases of drug development and help refine the use of therapeutic regimens after marketing, especially in populations that are under-represented in registration trials. For the management of hepatocellular carcinoma, early detection provides the best chance for curative therapies, whose success has been evaluated in numerous cohorts. The availability of novel systemic therapies, including kinase inhibitors and immunotherapies, has provided new treatment options and improved survival in patients with advanced stage hepatocellular carcinoma. Real-world longitudinal observational studies can help understand the long-term safety and effectiveness of these agents.

Belantamab Mafodotin for the Treatment of Multiple Myeloma: An Overview of the Clinical Efficacy and Safety.

Despite the introduction of immunomodulatory drugs (IMiDs), proteasome inhibitors (PIs), and, more recently, monoclonal antibodies (mAbs), in the chemotherapy regimens for newly diagnosed (NDMM) and relapsed/refractory MM (RRMM), the occurrence of drug resistance remains a challenge in MM patients. This is mainly in the advanced stage of the disease when treatments are limited, and the prognosis is abysmal. Nevertheless, novel molecules and therapeutic approaches are rapidly moving through the several phases of drug development and could address the need for new treatment options. The recent innovative B-cell maturation antigen (BCMA) targeted immunotherapies, such as belantamab mafodotin, the first-in-class monoclonal antibody-drug conjugate (ADC), induce an effective and durable response in triple-class refractory disease and to be approved in MM. In contrast with the other BCMA-targeted therapies as CAR T cells with a complex manufacturing process, and bispecific antibodies, both requiring inpatient hospitalization to monitor the occurrence of severe adverse events, belantamab mafodotin is an “off-the-shelf” drug that can be administered in an outpatient setting. Many belantamab mafodotin-based combinations are under evaluation in Phase I, II, and III clinical trials either late or in early RRMM patients. Ocular toxicity represents a peculiar side effect of belantamab mafodotin. This toxicity is generally manageable with adequate dose reductions or delays since most patients who developed keratopathy recovered on treatment and discontinued ADC are rare. Here, we described the most recent clinical data of belantamab mafodotin and discussed the possible leading role of this intriguing agent in the near future of MM treatment.

Chemical Classes Presenting Novel Antituberculosis Agents Currently in Different Phases of Drug Development: A 2010-2020 Review.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a curable airborne disease currently treated using a drug regimen consisting of four drugs. Global TB control has been a persistent challenge for many decades due to the emergence of drug-resistant Mtb strains. The duration and complexity of TB treatment are the main issues leading to treatment failures. Other challenges faced by currently deployed TB regimens include drug-drug interactions, miss-matched pharmacokinetics parameters of drugs in a regimen, and lack of activity against slow replicating sub-population. These challenges underpin the continuous search for novel TB drugs and treatment regimens. This review summarizes new TB drugs/drug candidates under development with emphasis on their chemical classes, biological targets, mode of resistance generation, and pharmacokinetic properties. As effective TB treatment requires a combination of drugs, the issue of drug-drug interaction is, therefore, of great concern; herein, we have compiled drug-drug interaction reports, as well as efficacy reports for drug combinations studies involving antitubercular agents in clinical development.

An automated approach to identify scientific publications reporting pharmacokinetic parameters.

Pharmacokinetic (PK) predictions of new chemical entities are aided by prior knowledge from other compounds. The development of robust algorithms that improve preclinical and clinical phases of drug development remains constrained by the need to search, curate and standardise PK information across the constantly-growing scientific literature. The lack of centralised, up-to-date and comprehensive repositories of PK data represents a significant limitation in the drug development pipeline.In this work, we propose a machine learning approach to automatically identify and characterise scientific publications reporting PK parameters from in vivo data, providing a centralised repository of PK literature. A dataset of 4,792 PubMed publications was labelled by field experts depending on whether in vivo PK parameters were estimated in the study. Different classification pipelines were compared using a bootstrap approach and the best-performing architecture was used to develop a comprehensive and automatically-updated repository of PK publications. The best-performing architecture encoded documents using unigram features and mean pooling of BioBERT embeddings obtaining an F1 score of 83.8% on the test set. The pipeline retrieved over 121K PubMed publications in which in vivo PK parameters were estimated and it was scheduled to perform weekly updates on newly published articles. All the relevant documents were released through a publicly available web interface (https://app.pkpdai.com) and characterised by the drugs, species and conditions mentioned in the abstract, to facilitate the subsequent search of relevant PK data. This automated, open-access repository can be used to accelerate the search and comparison of PK results, curate ADME datasets, and facilitate subsequent text mining tasks in the PK domain.

Computational prediction of photosensitizers’ toxicity

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, and to assess potential mutagenic impurities in development and degradants as part of life-cycle management. In this study, for the first time, in silico approaches were used to analyze the possible dark toxicity of photosensitive systems based on chlorin e6 and assessed possible toxicity of these compositions. By applying quantitative structure-activity relationship models (QSARs) and modeling adverse outcome pathways (AOPs), a potential toxic effect of water-soluble (chlorin e6 and chlorin e6 aminoamid) and hydrophobic (tetraphenylporphyrin) photosensitizers (PS) was predicted. Particularly, PSs’ protein binding ability, reactivity to form peptide adducts, glutathione conjugation, activity in dendritic cells, and gene expression activity in keratinocytes were explored. Using a metabolism simulator, possible PS metabolites were predicted and their potential toxicity was assessed as well. It was shown that all tested porphyrin PS and their predicted metabolites possess low activity in the mentioned processes and therefore are unable to cause significant adverse toxic effects under dark conditions.

Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19.

Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization (MR) analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2: P=1.6×10-6, IFNAR2: P=9.8×10-11, and IL-10RB: P=2.3×10-14) using cis-eQTL genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared eQTL signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.

Sex Proportionality in Pre-clinical and Clinical Trials: An Evaluation of 22 Marketing Authorization Application Dossiers Submitted to the European Medicines Agency.

This study assessed to what extent women were included in all phases of drug development; whether the clinical studies in the marketing authorization application dossiers include information per sex; and explored whether there are differences between women and men in the drugs' efficacy and safety. Data were extracted from dossiers submitted to the European Medicines Agency. Twenty-two dossiers of drugs approved between 2011 and 2015 for the treatment of various diseases were included. Female animals were included in only 9% of the pharmacodynamics studies, but female and male animals were included in all toxicology studies. Although fewer women than men were included in the clinical studies used to evaluate pharmacokinetics (PK) (29 to 40% women), all dossiers contained sex-specific PK parameter estimations. In the phase III trials, inclusion of women was proportional to disease prevalence for depression, epilepsy, thrombosis, and diabetes [participation to prevalence ratio (PPR) range: 0.91–1.04], but women were considered underrepresented for schizophrenia, hepatitis C, hypercholesterolemia, HIV, and heart failure (PPR range: 0.49-0.74). All dossiers contained sex-specific subgroup analyses of efficacy and safety. There seemed to be higher efficacy for women in one dossier and a trend toward lower efficacy in another dossier. More women had adverse events in both treatment (73.0 vs. 70.6%, p < 0.001) and placebo groups (69.5 vs. 65.5%, p < 0.001). In conclusion, women were included throughout all phases of clinical drug research, and sex-specific information was available in the evaluated dossiers. The included number of women was, however, not always proportional to disease prevalence rates.

A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies.

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.

Antimicrobial drugs bearing guanidine moieties: A review.

Compounds incorporating guanidine moieties constitute a versatile class of biologically interesting molecules with a wide array of applications. As such, guanidines have been exploited as privileged structural motifs in designing novel drugs for the treatment of various infectious and non-infectious diseases. In designing anti-infective agents, this moiety carries great appeal by virtue of attributes such as hydrogen-bonding capability and protonatability at physiological pH in the context of interaction with biological targets. This review provides an overview of recent advances in hit-to-lead development studies of antimicrobial guanidine-containing compounds with the aim to highlight their structural diversity and the pharmacological relevance of the moiety to drug activity, insofar as possible. In so doing, emphasis is put on chemical and microbiological properties of such compounds in relation to antibacterial, antifungal and antimalarial activities.

Aged rhesus monkeys: Cognitive performance categorizations and preclinical drug testing

Rodent models have facilitated major discoveries in neurobiology, however, the low success rate of novel medications in clinical trials have led to questions about their translational value in neuropsychiatric drug development research. For age-related disorders of cognition such as Alzheimer’ disease (AD) there is interest in moving beyond transgenic amyloid-β and/or tau-expressing rodent models and focusing more on natural aging and dissociating “healthy” from “pathological” aging to identify new therapeutic targets and treatments. In complex disorders such as AD, it can also be argued that animals with closer neurobiology to humans (e.g., nonhuman primates) should be employed more often particularly in the later phases of drug development. The purpose of the work described here was to evaluate the cognitive capabilities of rhesus monkeys across a wide range of ages in different delayed response tasks, a computerized delayed match to sample (DMTS) task and a manual delayed match to position (DMTP) task. Based on specific performance criteria and comparisons to younger subjects, the older subjects were generally less proficient, however, some performed as well as young subjects, while other aged subjects were markedly impaired. Accordingly, the older subjects could be categorized as aged “cognitively-unimpaired” or aged “cognitively-impaired” with a third group (aged-other) falling in between. Finally, as a proof of principle, we demonstrated using the DMTP task that aged cognitively-impaired monkeys are sensitive to the pro-cognitive effects of a nicotinic acetylcholine receptor (nAChR) partial agonist, encenicline, suggesting that nAChR ligands remain viable as potential treatments for age-related disorders of cognition.

Pharmaceutical portfolio optimization under cost uncertainty via chance constrained-type method

Project selection for a portfolio is a pivotal decision in the pharmaceutical industry. In this paper, we study a portfolio optimization problem for pharmaceutical companies considering the uncertainty of the cost of each phase of drug development and the specific value of the annual budget. The presented optimization model is suitable to make investment decisions for multi-phase drug development projects and a stochastic approach is applied to handle the uncertainty in the model. Post-optimality analysis for annual budget is studied. An illustrative example is included to demonstrate the presented approach.

Overcoming the impact of the COVID‐19 pandemic on oncology early phase trials and drug development in Asia—Experiences and perspectives of the Asian Oncology Early Phase 1 Consortium

AimThe significance and prioritization of early phase oncology trial continuation during a global pandemic is unknown. This study reported the outcomes, multiple challenges, and broad recommendations associated with the impact of the novel coronavirus disease 2019 (COVID‐19) on oncology early phase 1 trials—and on drug development in Asia—based on the experiences and perspectives of Asian oncology phase 1 centers.MethodsBetween March and April 2020 during the initial period of outbreak, the impact of COVID‐19 across oncology phase 1 sites in five Asian countries—China (Hong Kong), Japan, South Korea, Taiwan, and Singapore—was retrospectively analyzed.ResultsThere was no trial termination or treatment discontinuation in all five countries. Although the most common impact was new patient enrollment being placed on hold, which was based on pharmaceutical sponsors’ decision‐making, the situation varied per site. Most sites had no restrictions in place that would limit their ability to fully comply with the requirements of conducting the early phase studies. The number of protocol deviations during the pandemic was largely dependent on domestic transportation status during the outbreak rather than the ability of the clinical trial centers.ConclusionDetermining the risk to benefits ratio of patients with cancer who are enrolled in early phase 1 clinical trials under the unusual circumstances of a global pandemic is important. Specific guidance or guidelines on the conduct of early phase 1 clinical trials during public health emergencies that are based on the recent lessons learned is urgently required.

Drug development for cystic fibrosis.

The first regulatory approval for a drug developed specifically for cystic fibrosis (CF) occurred in 1993, and since then, several other drugs have been approved. Median predicted survival in people with CF in the United States has increased from approximately 30 years to 44.4 years over that same period. Highly effective modulators of the cystic fibrosis transmembrane conductance regulator became available to approximately 90% of people with CF ages 12 years and older in the United States in 2019 and in Europe in 2020. These transformative therapies will surely reduce morbidity and further extend longevity. The drug development pipeline is filled with therapies that address most aspects of CF disease. As survival and CF therapies advance, and the complexity of CF care increases, the process of drug development has become more sophisticated. In addition, detecting meaningful changes in outcome measures has become more difficult as the health status of people with CF improves. Innovative approaches are required to continue to advance drug development in CF. This review provides a general overview of drug development from the preclinical phase through Phase IV. Special considerations with respect to CF are integrated into the discussion of each phase of drug development. As CF care evolves, drug development must continue to evolve as well, until a one-time cure is available to all people with CF.

Short Communication on Phases of Drug Development

Short Communication on Phases of Drug Development

Koncept i upotreba populacionih farmakokinetičkih i farmakokinetičko/farmakodinamičkih modela u razvoju leka i kliničkoj praksi

Due to frequent clinical trial failures and consequently fewer new drug approvals, the need for improvement in drug development has, to a certain extent, been met using model-based drug development. Pharmacometrics is a part of pharmacology that quantifies drug behaviour, treatment response and disease progression based on different models (pharmacokinetic - PK, pharmacodynamic - PD, PK/PD models, etc.) and simulations. Regulatory bodies (European Medicines Agency, Food and Drug Administration) encourage the use of modelling and simulations to facilitate decision-making throughout all drug development phases. Moreover, the identification of factors that contribute to variability provides a basis for dose individualisation in routine clinical practice. This review summarises current knowledge regarding the application of pharmacometrics in drug development and clinical practice with emphasis on the population modelling approach.