Early Stages Of Drug Development Research Articles

Methods to Evaluate Skin Penetration In Vitro

Dermal and transdermal drug therapy is increasing in importance nowadays in drug development. To completely utilize the potential of this administration route, it is necessary to optimize the drug release and skin penetration measurements. This review covers the most well-known and up-to-date methods for evaluating the cutaneous penetration of drugs in vitro as a supporting tool for pharmaceutical research scientists in the early stage of drug development. The aim of this article is to present various experimental models used in dermal/transdermal research and summarize the novel knowledge about the main in vitro methods available to study skin penetration. These techniques are: Diffusion cell, skin-PAMPA, tape stripping, two-photon microscopy, confocal laser scanning microscopy, and confocal Raman microscopic method.

Toxicity prediction of small drug molecules of aryl hydrocarbon receptor using a proposed ensemble model

Quantitative structure-activity relationships and quantitative structure?property relationships have proved their usefulness for predicting toxicities of drug molecules regarding their biological activities. In silico toxicity prediction techniques are essential for reducing testing on rodents (in vivo) and for a less time-consuming and more cost-efficient alternative for the identification of toxic effects at an early stage of drug development. The authors aim to build a prediction model for better assessment of toxicity to quickly and efficiently test whether certain chemical compounds have the potential to disrupt the processes in the human body that may adversely affect human health. Here, we have proposed a computational method (in silico) for the toxicity prediction of small drug molecules using their various physicochemical properties (molecular descriptors) that can bind to the aryl hydrocarbon receptor. Pharmaceutical data exploration laboratory software is used for extracting the features of drug molecules. The dataset of the aryl hydrocarbon receptor contains 9008 drug molecules, where 1063 are active and 7945 are inactive, and each drug molecule contains 1444 features. It is a novel prediction model based on ensemble learning that can efficiently classify active (binding) and inactive (nonbinding) compounds of the dataset. In our proposed ensemble model, we primarily performed feature selection using the Boruta library in R, after which we resolved the class imbalance problem itself by ensemble learning where we divided the dataset into seven data frames, which have approximately equal numbers of active and inactive drug molecules. An ensemble model based upon the votes of seven random forest models is proposed, which gives an accuracy of 93.76%. K-fold cross-validation is conducted to measure the consistency of the model. Finally, the validity of the proposed ensemble model for some drug molecules of acquired immune deficiency syndrome therapy and androgen receptor has been proved.

Abstract 4120: Dose selection and dosing optimization of immune checkpoint inhibitors

Abstract Background: The differential mechanisms of action of immune checkpoint inhibitors and targeted therapies versus chemotherapy warrant new thinking regarding the conventional maximum-tolerated dose (MTD) approach. Dosing optimization of immune checkpoint inhibitors via flat-dosing compared with body-weight-based dosing reduces dosing errors, provides benefits for patients, and is advantageous for healthcare providers. A review of the strategies used for dose selection and optimization for approved checkpoint inhibitors is presented. Methods: Clinical pharmacology data on dose selection and optimization for FDA-approved immune checkpoint inhibitors from FDA Summary Basis of Approval documents, published research papers, and scientific meeting/congress presentations were summarized. Results: Of the 7 immune checkpoint inhibitors approved by the FDA and/or the EMA, none had an MTD determined during their phase 1 dose-escalation programs. Dose selections were based on the integration of preclinical and clinical information, including in vitro and/or in vivo receptor target engagement, preclinical and clinical pharmacokinetic/pharmacodynamic data, and efficacy and safety data. Chosen regimens were confirmed by exposure–response analyses using phase 3 data. Post-approval optimization strategies were employed to refine the dosing schedules (eg, provide beneficial flexibility to patients and caregivers). Interestingly, 6 of the 7 currently approved immune checkpoint inhibitors use a flat-dosing regimen, with 4 changing prescribing information to flat-dosing after the initial approval of dosing by body weight, and 3 of them with fixed dosing in the initial submission. Conclusion: The new era of oncology therapy, epitomized by the advent of immuno-oncology and molecular targeted agents, has been accompanied by a shift from the MTD-based approach developed for cytotoxic drugs to those that aim to achieve optimized doses and regimens using the totality of preclinical and clinical data. Close collaborations between discovery and biomarker scientists, clinicians, clinical pharmacologists, and pharmacometricians facilitate the selection of optimal regimens. As the number of indications covered by immune checkpoint inhibitors increases, and clinical experience accumulates, it is likely that the transition from body-weight-based to fixed dosing will move to earlier stages of drug development. These efforts enable the early identification of the optimal dosing regimens, reducing development time and increasing the probability of success. Importantly, the utilization of optimized dosing development paradigms enables the development of oncology drugs with improved efficacy and tolerability profiles, thus providing increased benefits to patients and healthcare providers. Citation Format: Jennifer Sheng, Kinjal Sanghavi, Yali Liang, Shivani Srivastava, Akintunde Bello. Dose selection and dosing optimization of immune checkpoint inhibitors [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 4120.

De-Risking Early-Stage Drug Development With a Bespoke Lattice Energy Predictive Model: A Materials Science Informatics Approach to Address Challenges Associated With a Diverse Chemical Space

The solid-state properties of new chemical entities are critical to the stability and bioavailability of pharmaceutical drug products. The stability of the solid-state packing is described by the packing energy and an accurate prediction of this property for drug molecules would therefore be desirable. However, this has been difficult to achieve because of the lack of fundamental thermodynamic data on drug molecules. A potential solution would be to use calculated lattice energies to build a model and design molecules with desired physicochemical properties from an early stage, aligning with a “design by first intent” strategy for physicochemical properties. We first demonstrate the high correlation and interchangeability between QSPR models built using calculated lattice energies and experimental sublimation enthalpies for small organic molecules. We then present a QSPR model trained on in-house molecules using lattice energies calculated from crystal structures. The result is a model that enables fast prediction of the lattice energies of in-house molecules from 2-D molecular structure with reasonable accuracy (R2 = 0.92, root mean square error = 3.58 kcal/mol). We explore the model elements to improve our understanding of the molecular properties that contribute to lattice energy and then suggest potential cross-industry aspects that may enhance the application of the concept.

Computational models for the prediction of adverse cardiovascular drug reactions

BackgroundPredicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement.MethodsIn the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets.ResultsThis is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features.ConclusionsThe results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development.

Quantitative prediction of hepatic CYP3A activity using endogenous markers in healthy subjects after administration of CYP3A inhibitors or inducers

Accurate prediction of cytochrome P450 (CYP) 3A activity in the early stage of drug development and in clinical practice is important. This study aimed to evaluate the previously constructed CYP3A activity prediction model after administration of CYP3A inhibitors and inducers and to modify the model for better prediction of CYP3A activity. Healthy male subjects received the following study drugs during three study periods: midazolam alone (control phase); midazolam with 200 mg of itraconazole (CYP3A inhibition phase); and midazolam with 150 mg of rifampicin (CYP3A induction phase). We quantified the concentrations of several endogenous CYP3A markers in both urine and plasma using gas chromatography-mass spectrometry. The urinary markers, including 6β-hydroxy (OH)-cortisol/cortisol, 6β-OH-cortisone/cortisone, 16α-OH-dehydroepiandrosterone (DHEA)/DHEA, 16α-OH-androstenedione (A-dione)/A-dione and 7β-OH-DHEA/DHEA, were significantly correlated with midazolam clearance in both the CYP3A inhibition and induction phases. We constructed a statistical prediction model after integrating data from a previous study to predict midazolam clearance as follows: Ln(midazolam clearance) = 2.5545 + 0.3988 × ln(7β-OH-DHEA/DHEA) + 0.1984 × ln(16α-OH-DHEA/DHEA) + 0.5031 × ln(6β-OH-cortisol/cortisol) – 0.1261 [ln(7β-OH-DHEA/DHEA) × ln(6β-OH-cortisol/cortisol)] (r2 = 0.75). We suggest that quantitating endogenous markers in vivo coupled with the statistical prediction model may be useful for predicting CYP3A parameters.

EPTRI – European Paediatric Translational Research Infrastructure. Bridging the gaps of the paediatric excellence medicine

Development of age appropriate medicines for children is one of the major challenge of our century. Historically, research of new paediatric drugs has been neglected due to poor industrial interest and limited public and private investments. The ID-EPTRI project is aimed to bridge the existing gaps in the paediatric medicine that stop the progress, from the early stage drug development phases to be translated into paediatric use of medicines, through a new paediatric Research Infrastructure. To reach this goal, EPTRI has developed and disseminated a survey in order to identify the gaps and map the competences of the excellence of the paediatric research in pan-European countries that will be the potential service providers of the new Research Infrastructure. EPTRI will network all the available competences and technologies useful to the paediatric research, creating an open science space allowing top-level researchers to work together.

Stem cell models as an in vitro model for predictive toxicology.

Adverse drug reactions (ADRs) are the unintended side effects of drugs. They are categorised as either predictable or unpredictable drug-induced injury and may be exhibited after a single or prolonged exposure to one or multiple compounds. Historically, toxicology studies rely heavily on animal models to understand and characterise the toxicity of novel compounds. However, animal models are imperfect proxies for human toxicity and there have been several high-profile cases of failure of animal models to predict human toxicity e.g. fialuridine, TGN1412 which highlight the need for improved predictive models of human toxicity. As a result, stem cell-derived models are under investigation as potential models for toxicity during early stages of drug development. Stem cells retain the genotype of the individual from which they were derived, offering the opportunity to model the reproducibility of rare phenotypes in vitro. Differentiated 2D stem cell cultures have been investigated as models of hepato- and cardiotoxicity. However, insufficient maturity, particularly in the case of hepatocyte-like cells, means that their widespread use is not currently a feasible method to tackle the complex issues of off-target and often unpredictable toxicity of novel compounds. This review discusses the current state of the art for modelling clinically relevant toxicities, e.g. cardio- and hepatotoxicity, alongside the emerging need for modelling gastrointestinal toxicity and seeks to address whether stem cell technologies are a potential solution to increase the accuracy of ADR predictivity in humans.

Prediction of Human Nonlinear Pharmacokinetics of a New Bcl-2 Inhibitor Using PBPK Modeling and Interspecies Extrapolation Strategy.

S 55746 ((S)-N-(4-hydroxyphenyl)-3-(6-(3-(morpholinomethyl)-1,2,3,4-tetrahydroisoquinoline-2-carbonyl)benzo[d][1,3]dioxol-5-yl)-N-phenyl-5,6,7,8-tetrahydroindolizine-1-carboxamide) is a new selective Bcl-2 (B-cell lymphoma 2) inhibitor developed by Servier Laboratories and used to restore apoptosis functions in cancer patients. The aim of this work was to develop a translational approach using physiologically based (PB) pharmacokinetic (PK) modeling for interspecies extrapolation to anticipate the nonlinear PK behavior of this new compound in patients. A PBPK mouse model was first built using a hybrid approach, defining scaling factors (determined from in vitro data) to correct in vitro clearance parameters and predicted Kp (partition coefficient) values. The qualification of the hybrid model using these empirically determined scaling factors was satisfactorily completed with rat and dog data, allowing extrapolation of the PBPK model to humans. Human PBPK simulations were then compared with clinical trial data from a phase 1 trial in which the drug was given orally and daily to cancer patients. Human PBPK predictions were within the 95% prediction interval for the eight dose levels, taking into account both the nonlinear dose and time dependencies occurring in S 55746 kinetics. Thus, the proposed PK interspecies extrapolation strategy, based on preclinical and in vitro information and physiologic assumptions, could be a useful tool for predicting human plasma concentrations at the early stage of drug development.

Small molecules hold sway at DCAT Week

The Drug, Chemical, & Associated Technologies Association (DCAT) Week, an annual meeting for the pharmaceutical services sector and the drug companies it serves, has dispersed to several New York City locations now that its traditional home, the Waldorf Astoria New York, is under renovation. But the bustle of conference talks and private meetings culminating in a black-tie dinner on March 21 has lost none of its intensity, nor its focus. The same might be said for the sector, which in recent years has seen contract development and manufacturing organizations (CDMOs) add nonchemistry services such as biologics and final-dose formulation to their traditional business of synthesizing active pharmaceutical ingredients (APIs). Investments announced at DCAT Week make clear that small-molecule drug production is still central. Cambrex epitomizes the expanding CDMO. The US company recently acquired Avista Pharma Solutions, an early-stage drug development firm, and Halo Pharma, a finished-dose drug contractor, for...

Building in-house PBPK modelling tools for oral drug administration from literature information.

The interest in using physiologically-based pharmacokinetic (PBPK) models as a support to the drug development decision making process has rapidly increased in the last years. These kind of models are examples of the “bottom up” modelling strategy, which progressively integrates into a mechanistic framework different information as soon as they become available along the drug development. For this reason PBPK models can be used with different aims, from the early stages of drug development up to the clinical phases. Different software tools are nowadays available. They can be categorized in “designed software” and “open software”. The first ones typically include commercial platforms expressly designed to implement PBPK models, in which the model structure is pre-defined, assumptions are generally not explicitly declared and equations are hidden to the user. Even if the software is validated and routinely used in the pharmaceutical industry, sometimes they do not allow working with the flexibility needed to cope with specific applications/tasks. For this reason, some scientists prefer to define and implement their own PBPK tool in “open” software. This paper shows how to build an in-house PBPK tool from species-related physiological information available in the literature and a limited number of drug specific parameters generally made available by the drug development process. It also reports the results of an evaluation exercise that compares simulated plasma concentration-time profiles and related pharmacokinetic (PK) parameters (i.e., AUC, Cmax and Tmax) with literature and in-house data. This evaluation involved 25 drugs with different physico-chemical properties, intravenously or orally administrated in three different species (i.e., rat, dog and man). The comparison shows that model predictions have a good degree of accuracy, since the average fold error for all the considered PK parameters is close to 1 and only in few cases the fold error is greater than 2. In summary, the paper demonstrates that addressing specific aims when needed is possible by creation of in-house PBPK tools with satisfactory performances and it provides some suggestions how to do that.

From Molecule to Patient and Ways to Get the Dose Precisely Right

From Molecule to Patient and Ways to Get the Dose Precisely Right

Whole-Body Physiologically Based Pharmacokinetic Modeling of Trastuzumab and Prediction of Human Pharmacokinetics

In the present study, we evaluated the pharmacokinetics (PK) of trastuzumab and sought to predict human PK based on animal studies, through the use of optical imaging and a whole-body physiologically based pharmacokinetic (WB-PBPK) modeling approach. The PK study was conducted in 24 mice, where serial blood samples were withdrawn and major organs were isolated after the last blood withdrawal. The drug concentrations in blood and major organs were measured via optical imaging. The WB-PBPK model was constructed using known physiological values including the volumes of major organs and blood/lymphatic flow. The NONMEM software (version 7.3) was used to determine tissue partition coefficients. Using the WB-PBPK model, a clinical trial simulation was performed with reference to human physiological values acquired from the literature. The simulated human PK was then compared with the actual PK observed in the previous study in which healthy male subjects received 6 mg/kg trastuzumab (Herceptin®) via intravenous route. The ratio of the simulated versus observed area under the concentration-time curve was 1.02 and that of maximal concentration was 0.72. The current study describes the potential synergistic applications of WB-PBPK and optical imaging in human PK prediction based on preclinical data obtained in early-stage drug development.

Protein-ligand interaction fingerprints for accurate prediction of dissociation rates of p38 MAPK Type II inhibitors.

Binding/unbinding kinetics are key determinants of drug potencies. However, there are still a lot of challenges in predicting kinetic properties during early-stage drug development. In this work, position-restrained molecular dynamics simulations combined with energy decomposition were applied to extract protein-ligand interaction (PLI) fingerprints along the unbinding pathway of 20 p38 mitogen-activated protein kinase (p38 MAPK) Type II inhibitors. The results showed that the electrostatic and/or van der Waals interaction fingerprints at three key positions can be used for accurate prediction of the dissociation rate constants (koff) of p38 MAPK Type II inhibitors. The strategy proposed in this paper can provide not only an efficient method of predicting the dissociation rates of the p38 MAPK Type II inhibitors, but also the atom-level mechanism of enthalpy-driven unbinding process.

Detection of Drug-Induced Cholestasis Potential in Sandwich-Cultured Human Hepatocytes.

Drug-induced cholestasis poses a major hurdle for the pharmaceutical industry as it is one the primary mechanisms of drug-induced liver injury. Hence, detection of drug-induced cholestasis during the early stages of drug development is of utmost importance. The most commonly used in vitro models rely on the extent of inhibition of bile salt export pump-mediated taurocholic acid transport, thereby assuming that drug-induced cholestasis mechanisms are merely restricted to the interaction with this sole hepatic transporter. Sandwich-cultured human hepatocytes represent a more holistic in vitro tool to investigate drug-induced cholestasis as they preserve all relevant disposition pathways and cellular functions involved in bile acid homeostasis. We developed and validated a sandwich-cultured human hepatocytes-based in vitro assay which is able to identify compounds causing cholestasis by altering bile acid disposition. The in vitro cholestatic potential is expressed by calculating a drug-induced cholestasis index value, which reflects the relative residual urea formation of sandwich-cultured human hepatocytes co-incubated with bile acids and test compound as compared to sandwich-cultured human hepatocytes treated with test compound alone. In addition, a safety margin can be calculated to determine the in vivo risk for cholestasis based on the determination of the drug-induced cholestasis index at various concentrations and the peak plasma concentration of the drug candidate. This chapter outlines the various steps involved in performing our sandwich-cultured human hepatocytes-based in vitro assay.

Evolving role of modeling and simulation in drug development.

Pharmacokinetic-pharmacodynamic model is a kind of language that quantitatively describes the drug-related outcomes in the form of mathematical formula. Various outcomes can be subjected to modeling analysis if they can be expressed in numbers. Empirical models have been widely and successfully applied in drug development and research. However, a more competitive drug development environment requires more accurate and predictive models in the early stages of drug development. Accordingly, the subjects of PK-PD modeling have been extended from clinical data to preclinical and in vitro data in the discovery stage. More mechanistic and predictive models, such as physiologically based pharmacokinetic and quantitative system-based pharmacology models, are being increasingly used owing to the growing need to characterize drugs more accurately at the earliest. This tutorial briefly introduces the essential concepts of PK-PD modeling and simulation and describes the recent changing roles of PK-PD model for application in novel drug development process.

Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers.

Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.

The R Package MAMS for Designing Multi-Arm Multi-Stage Clinical Trials

In the early stages of drug development there is often uncertainty about the most promising among a set of different treatments, different doses of the same treatment, or combinations of treatments. Multi-arm multi-stage (MAMS) clinical studies provide an efficient solution to determine which intervention is most promising. In this paper we discuss the R package MAMS that allows designing such studies within the group-sequential framework. The package implements MAMS studies with normal, binary, ordinal, or timeto-event endpoints in which either the single best treatment or all promising treatments are continued at the interim analyses. Additionally unexpected design modifications can be accounted for via the use of the conditional error approach. We provide illustrative examples of the use of the package based on real trial designs.

Natural radioprotectors and their impact on cancer drug discovery

Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.

Reconsidering mass spectrometric fragmentation in electron ionization mass spectrometry - new insights from recent instrumentation and isotopic labelling exemplified by ketoprofen and related compounds.

In various fields of chemical analyses, structurally unknown analytes are considered. Proper structure confirmation may be challenged by the low amounts of analytes that are available, e.g. in early stage drug development, in metabolism studies, in toxicology or in environmental analyses. In these cases, mass spectrometric techniques are often used to build up structure proposals for these unknowns. Fragmentation reactions in mass spectrometry are known to follow definite pathways that may help to assign structural elements by fragment ion recognition. This work illustrates an investigation of fragmentation reactions for gas chromatography/electron ionization mass spectrometric characterization of benzophenone derivatives using the analgesic drug ketoprofen and seven of its related compounds as model compounds. Deuteration and 18 O-labelling experiments along with high-resolution accurate mass and tandem mass spectrometry (MS/MS) were used to further elucidate fragmentation pathways and to substantiate rationales for structure assignments. Low-energy ionization was investigated to increase confidence in the identity of the molecular ion. The high-resolution mass analyses yielded unexpected differences that led to reconsideration of the proposals. Site-specific isotopic labelling helped to directly trace back fragment ions to their respective structural elements. The proposed fragmentation pathways were substantiated by MS/MS experiments. The described method may offer a perspective to increase the level of confidence in unknown analyses, where reference material is not (yet) available.