Pharmacokinetic/ pharmacodynamic-driven drug development

Evaluation of gender differences in the pharmacokinetics of oral zileuton nanocrystalline formulation using a rat model

Zileuton is a leukotriene inhibitor used to treat asthma. As a BCS class II drug it exhibits challenges with solubility which likely impact its absorption. As patient gender significantly impacts the pharmacokinetics of many drugs, this study aimed to investigate potential gender-based pharmacokinetic differences after oral zileuton administration in rats. Male and female Sprague Dawley rats received single oral gavage doses of pure zileuton as an active pharmaceutical ingredient (30 mg/kg body weight (bw)), physical mixture (PM; at 30 mg/kg bw of the formulation contains zileuton, kollidon VA64 fine, dowfax2A1 and trehalose), and nanocrystalline formulation of zileuton (NfZ; at 30 mg/kg bw of the formulation). Plasma, tissue, and urine concentrations were quantified using high performance liquid chromatography (HPLC). Noncompartmental pharmacokinetic analysis showed higher zileuton levels in the plasma of female versus male rats across all evaluated forms of zileuton (API, PM, and NfZ). Female rats demonstrated higher peak plasma concentrations (Cmax) and increased area under the plasma concentration-time curve (AUC) relative to males, regardless of formulation. These findings reveal substantial gender disparities in the pharmacokinetics of zileuton in the rat model. This study emphasizes the critical need to evaluate gender differences during preclinical drug development to enable gender-based precision dosing strategies for equivalent efficacy/safety outcomes in male and female patients. Additional studies are warranted to investigate underlying mechanisms of such pharmacokinetic gender divergences.

Molecular association of Candida albicans and vulvovaginal candidiasis: focusing on a solution

Candida albicans-mediated vulvovaginal candidiasis (VVC) is a significant challenge in clinical settings, owing to the inefficacy of current antifungals in modulating virulence, development of resistance, and poor penetration into the biofilm matrix. Various predisposition factors are molecular drivers that lead to the dysbiosis of normal microflora of the vagina, upregulation of central metabolic pathways, morphogenesis, hyphal extension, adhesion, invasion, and biofilm formation leading to chronic infection and recurrence. Hence, it is crucial to understand the molecular mechanism behind the virulence pathways driven by those drivers to decode the drug targets. Finding innovative solutions targeting fungal virulence/biofilm may potentiate the antifungals at low concentrations without affecting the recurrence of resistance. With this background, the present review details the critical molecular drivers and associated network of virulence pathways, possible drug targets, target-specific inhibitors, and probable mode of drug delivery to cross the preclinical phase by appropriate in vivo models.

Algae-Derived Natural Products in Diabetes and Its Complications-Current Advances and Future Prospects

Diabetes poses a significant global health challenge, necessitating innovative therapeutic strategies. Natural products and their derivatives have emerged as promising candidates for diabetes management due to their diverse compositions and pharmacological effects. Algae, in particular, have garnered attention for their potential as a source of bioactive compounds with anti-diabetic properties. This review offers a comprehensive overview of algae-derived natural products for diabetes management, highlighting recent developments and future prospects. It underscores the pivotal role of natural products in diabetes care and delves into the diversity of algae, their bioactive constituents, and underlying mechanisms of efficacy. Noteworthy algal derivatives with substantial potential are briefly elucidated, along with their specific contributions to addressing distinct aspects of diabetes. The challenges and limitations inherent in utilizing algae for therapeutic interventions are examined, accompanied by strategic recommendations for optimizing their effectiveness. By addressing these considerations, this review aims to chart a course for future research in refining algae-based approaches. Leveraging the multifaceted pharmacological activities and chemical components of algae holds significant promise in the pursuit of novel antidiabetic treatments. Through continued research and the fine-tuning of algae-based interventions, the global diabetes burden could be mitigated, ultimately leading to enhanced patient outcomes.

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose. Despite this awareness, most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modeling efforts that support MIDD and thus yields greater fidelity in decision-making. Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy. We highlight early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach.

Dendrophthoe falcata (L.f.) Ettingsh. and Dendrophthoe pentandra (L.) Miq.: A review of traditional medical uses, phytochemistry, pharmacology, toxicity, and applications

Dendrophthoe falcata (L.f.) Ettingsh. (DF) and Dendrophthoe pentandra (L.) Miq. (DP) have been traditionally used for the treatment of various ailments, such as cancer, ulcers, asthma, paralysis, skin diseases, tuberculosis, and menstrual troubles, in the ethnomedicinal systems of India and Indonesia. Currently, the chemical structures of 46 compounds have been elucidated from DF and DP, including flavonoids, triterpenes, tannins, steroids, open-chain aliphatics, benzyl derivates, and cyclic chain derivatives. In vitro assays have revealed their anti-tumor and anti-microbial activities. In vivo studies have unraveled their pharmacological properties against tumors, depression, fertility disorders, inflammatory responses, and so on. Additionally, their weak toxicity to rats and brine shrimp, as well as their promising applications for pharmaceutical preparations and combined medication, were also revealed. Herein, we not only recapitulated traditional medical uses, phytochemistry, pharmacology, toxicity, and applications of DF and DP but also discussed current research limitations and future perspectives, which are instructive for those interested in them and are committed to advancing parasitic plants to the Frontier of phytomedicine. We highlighted that DF and DP will become promising medical plants rather than being discarded as notorious pests, provided that more and deeper research is undertaken.

Pharmacophore-Model-Based Virtual-Screening Approaches Identified Novel Natural Molecular Candidates for Treating Human Neuroblastoma

The mortality of cancer patients with neuroblastoma is increasing due to the limited availability of specific treatment options. Few drug candidates for combating neuroblastoma have been developed, and identifying novel therapeutic candidates against the disease is an urgent issue. It has been found that muc-N protein is amplified in one-third of human neuroblastomas and expressed as an attractive drug target against the disease. The myc-N protein interferes with the bromodomain and extraterminal (BET) family proteins. Pharmacologically inhibition of the protein potently depletes MYCN in neuroblastoma cells. BET inhibitors target MYCN transcription and show therapeutic efficacy against neuroblastoma. Therefore, the study aimed to identify potential inhibitors against the BET family protein, specifically Brd4 (brodamine-containing protein 4), to hinder the activity of neuroblastoma cells. To identify effective molecular candidates against the disease, a structure-based pharmacophore model was created for the binding site of the Brd4 protein. The pharmacophore model generated from the protein Brd4 was validated to screen potential natural active compounds. The compounds identified through the pharmacophore-model-based virtual-screening process were further screened through molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, and molecular dynamics (MD) simulation approach. The pharmacophore-model-based screening process initially identified 136 compounds, further evaluated based on molecular docking, ADME analysis, and toxicity approaches, identifying four compounds with good binding affinity and lower side effects. The stability of the selected compounds was also confirmed by dynamic simulation and molecular mechanics with generalized Born and surface area solvation (MM-GBSA) methods. Finally, the study identified four natural lead compounds, ZINC2509501, ZINC2566088, ZINC1615112, and ZINC4104882, that will potentially inhibit the activity of the desired protein and help to fight against neuroblastoma and related diseases. However, further evaluations through in vitro and in vivo assays are suggested to identify their efficacy against the desired protein and disease.

The Combination of Electrochemistry and Microfluidic Technology in Drug Metabolism Studies

Drugs are metabolized within the liver (pH 7.4) by phase I and phase II metabolism. During the process, reactive metabolites can be formed that react covalently with biomolecules and induce toxicity. Identifying and detecting reactive metabolites is an important part of drug development. Preclinical and clinical investigations are conducted to assess the toxicity and safety of a new drug candidate. Electrochemistry coupled to mass spectrometry is an ideal complementary technique to the current preclinical studies, a pure instrumental approach without any purification steps and tedious protocols. The combination of microfluidics with electrochemistry towards the mimicry of drug metabolism offers portability, low volume of reagents and faster reaction times. This review explores the development of microfluidic electrochemical cells for mimicking drug metabolism.

Towards in vivo photomediated delivery of anticancer peptides: Insights from pharmacokinetic and -dynamic data

An in vivo study of a photoswitchable cytotoxic peptide LMB040 has been undertaken on a chemically induced hepatocellular carcinoma model in immunocompetent rats. We analysed the pharmacokinetic profile of the less toxic photoform ("ring-closed" dithienylethene) of the compound in tumors, plasma, and healthy liver. Accordingly, the peptide can reach a tumor concentration sufficiently high to exert a cytotoxic effect upon photoconversion into the more active ("ring-open") photoform. Tissue morphology, histology, redox state of the liver, and hepatic biochemical parameters in blood serum were analysed upon treatment with (i) the less active photoform, (ii) the in vivo light-activated alternative photoform, and (iii) compared with a reference chemotherapeutic 5-fluorouracil. We found that application of the less toxic form followed by a delayed in vivo photoconversion into the more toxic ring-open form of LMB040 led to a higher overall survival of the animals, and signs of enhanced immune response were observed compared to the untreated animals.

The importance of pharmacokinetics and pharmacodynamics in antimicrobial drug development and their influence on the success of agents developed to combat resistant gram negative pathogens: A review

A deep understanding of an antimicrobial’s critical pharmacokinetic and pharmacodynamic properties is crucial towards optimizing its use in patients and bolstering the drug development program. With the growing threat of antimicrobial resistance and decline in antimicrobial development, the advancement of complex and rigorous pharmacokinetic and pharmacodynamic studies over a short time span has renewed confidence in the value of pharmacokinetic and pharmacodynamic studies and allowed it to become fundamental component of a robust drug development program with high chances of successful approval. In addition, recent guidance by various regulatory bodies have reinforced that a strong and dedicated focus on pharmacokinetics and pharmacodynamics throughout research and development lead to the use of an optimized dosing regimen in Phase 3 trials, improving the probability of drug approval. The objective of this review is to demonstrate the importance of pharmacokinetic and pharmacodynamic studies in the drug development decision-making process by highlighting the developments in pharmacokinetic and pharmacodynamic methods and discuss the role of pharmacokinetic and pharmacodynamic studies in antimicrobial successes and failures.

A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: computational tools and recent advancements

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches.

Combination therapy for cerebral ischemia: do progesterone and noscapine provide better neuroprotection than either alone in the treatment?

Ischemic stroke presents multifaceted pathological outcomes with overlapping mechanisms of cerebral injury. High mortality and disability with stroke warrant a novel multi-targeted therapeutic approach. The neuroprotection with progesterone (PG) and noscapine (NOS) on cerebral ischemia-reperfusion (I-R) injury was demonstrated individually, but the outcome of combination treatment to alleviate cerebral damage is still unexplored. Randomly divided groups of rats (n = 6) were Sham-operated, I-R, PG (8 mg/kg), NOS (10 mg/kg), and PG + NOS (8 mg/kg + 10 mg/kg). The rats were exposed to bilateral common carotid artery occlusion, except Sham-operated, to investigate the therapeutic outcome of PG and NOS alone and in combination on I-R injury. Besides the alterations in cognitive and motor abilities, we estimated infarct area, oxidative stress, blood-brain barrier (BBB) permeability, and histology after treatment. Pharmacokinetic parameters like Cmax, Tmax, half-life, and AUC0-t were estimated in biological samples to substantiate the therapeutic outcomes of the combination treatment. We report PG and NOS prevent loss of motor ability and improve spatial memory after cerebral I-R injury. Combination treatment significantly reduced inflammation and restricted infarction; it attenuated oxidative stress and BBB damage and improved grip strength. Histopathological analysis demonstrated a significant reduction in leukocyte infiltration with the most profound effect in the combination group. Simultaneous analysis of PG and NOS in plasma revealed enhanced peak drug concentration, improved AUC, and prolonged half-life; the drug levels in the brain have increased significantly for both. We conclude that PG and NOS have beneficial effects against brain damage and the co-administration further reinforced neuroprotection in the cerebral ischemia-reperfusion injury.

A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges

Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.

Synthesis of a novel glibenclamide-pioglitazone hybrid compound and its effects on glucose homeostasis in normal and insulin-resistant rats

A new library of hybrid compounds that combine the functional parts of glibenclamide and pioglitazone was designed and developed. Compounds were screened for their antihyperglycemic effects on the glucose tolerance curve. This approach provided a single molecule that optimizes the pharmacological activities of two drugs used for the treatment of diabetes mellitus type 2 (DM2) and that have distinct biological activities, potentially minimizing the adverse effects of the original drugs. From a total of 15 compounds, 7 were evaluated in vivo; the compound 2; 4- [2- (2-phenyl-4-oxo-1,3-thiazolidin-3-yl) ethyl] benzene-1-sulfonamide (PTEBS) was selected to study its mechanism of action on glucose and lipid homeostasis in acute and chronic animal models related to DM2. PTEBS reduced glycemia and increased serum insulin in hyperglycemic rats, and elevated in vitro insulin production from isolated pancreatic islets. This compound increased the glycogen content in hepatic and muscular tissue. Moreover, PTEBS stimulated the uptake of glucose in soleus muscle through a signaling pathway similar to that of insulin, stimulating translocation and protein synthesis of glucose transporter 4 (GLUT4). PTEBS was effective in increasing insulin sensitivity in resistance rats by stimulating increased muscle glucose uptake, among other mechanisms. In addition, this compound reduced total triglycerides in a tolerance test to lipids and reduced advanced glycation end products (AGES), without altering lactate dehydrogenase (LDH) activity. Thus, we suggest that PTEBS may have similar effects to the respective prototypes, which may improve the therapeutic efficacy of these molecules and decrease adverse effects in the long-term.

Developing Analytical Applications for Parahydrogen Hyperpolarization: Urinary Elimination Pharmacokinetics of Nicotine

Nuclear magnetic resonance spectroscopy (NMR) is a valuable analytical tool with applications in a vast array of research fields from chemistry and biology to medicine and beyond. NMR is renowned for its straightforward data interpretation and quantitative properties, making it attractive for pharmacokinetic applications, where drug metabolism pathways, concentrations, and kinetics need to be evaluated. However, pharmacologically active compounds and their metabolites in biofluids often appear in minute concentrations, well below the detection limit of NMR. Herein, we demonstrate how parahydrogen hyperpolarization overcomes this sensitivity barrier, allowing us to detect mid-nanomolar concentrations of a drug and a drug metabolite in a biofluid matrix. The performance of the method is demonstrated by monitoring nicotine and cotinine urinary elimination, reflected by their concentrations in urine during the onset and withdrawal from nicotine consumption. An NMR limit of detection of 0.1 μM and a limit of quantitation of 0.7 μM is achieved in a practical pharmacokinetics scenario where precise quantitative and qualitative analysis is desired.

Auricular Vagus Neuromodulation-A Systematic Review on Quality of Evidence and Clinical Effects

Background: The auricular branch of the vagus nerve runs superficially, which makes it a favorable target for non-invasive stimulation techniques to modulate vagal activity. For this reason, there have been many early-stage clinical trials on a diverse range of conditions. These trials often report conflicting results for the same indication. Methods: Using the Cochrane Risk of Bias tool we conducted a systematic review of auricular vagus nerve stimulation (aVNS) randomized controlled trials (RCTs) to identify the factors that led to these conflicting results. The majority of aVNS studies were assessed as having "some" or "high" risk of bias, which makes it difficult to interpret their results in a broader context. Results: There is evidence of a modest decrease in heart rate during higher stimulation dosages, sometimes at above the level of sensory discomfort. Findings on heart rate variability conflict between studies and are hindered by trial design, including inappropriate washout periods, and multiple methods used to quantify heart rate variability. There is early-stage evidence to suggest aVNS may reduce circulating levels and endotoxin-induced levels of inflammatory markers. Studies on epilepsy reached primary endpoints similar to previous RCTs testing implantable vagus nerve stimulation therapy. Preliminary evidence shows that aVNS ameliorated pathological pain but not evoked pain. Discussion: Based on results of the Cochrane analysis we list common improvements for the reporting of results, which can be implemented immediately to improve the quality of evidence. In the long term, existing data from aVNS studies and salient lessons from drug development highlight the need for direct measures of local neural target engagement. Direct measures of neural activity around the electrode will provide data for the optimization of electrode design, placement, and stimulation waveform parameters to improve on-target engagement and minimize off-target activation. Furthermore, direct measures of target engagement, along with consistent evaluation of blinding success, must be used to improve the design of controls-a major source of concern identified in the Cochrane analysis. The need for direct measures of neural target engagement and consistent evaluation of blinding success is applicable to the development of other paresthesia-inducing neuromodulation therapies and their control designs.

A novel semi-mechanistic tumor growth fraction model for translation of preclinical efficacy of anti-glypican 3 antibody drug conjugate to human

The growing fraction (GF) of tumor has been reported as one of the predictive markers of the efficacy of chemotherapeutics. Therefore, a semi-mechanistic model has been developed that describes tumor growth on the basis of cell cycle, allowing the incorporation of the GF of a tumor in pharmacokinetic/pharmacodynamic (PK/PD) modeling. Efficacy data of anti-glypican 3 (GPC3) antibody drug conjugate (ADC) in a hepatocellular carcinoma (HCC) patient derived xenograft (PDX) model was used for evaluation of this proposed model. Our model was able to describe the kinetics of growth inhibition of HCC PDX models following treatment with anti-GPC3 ADC remarkably well. The estimated tumurostatic concentrations were used in tandem with human PKs translated from cynomolgus monkey for prediction of the efficacious dose. The projected efficacious human dose of anti-GPC3 ADC was in the range 0.20-0.63 mg/kg for the Q3W dosing regimen, with a median dose of 0.50 mg/kg. This publication is the first step in evaluating the applicability of GF in PK/PD modeling of ADCs. The authors are hopeful that incorporation of GF will result in an improved translation of the preclinical efficacy of ADCs to clinical settings and thereby better prediction of the efficacious human dose.

Kinetic analysis of the accumulation of a half-sandwich organo-osmium pro-drug in cancer cells

The organo-osmium half-sandwich complex [(η6-p-cymene)Os(Ph-azopyridine-NMe2)I]+ (FY26) exhibits potent antiproliferative activity towards cancer cells and is active in vivo. The complex is relatively inert, but rapidly activated in cells by displacement of coordinated iodide. Here, we study time-dependent accumulation of FY26 in A2780 human ovarian cancer cells at various temperatures in comparison with the chlorido metabolite [(η6-p-cymene)Os(Ph-azopyridine-NMe2)Cl]+ (FY25). Mathematical models described the time evolution of FY26 and FY25 intracellular and extracellular concentrations taking into account both cellular transport (influx and efflux) and the intracellular conversion of FY26 to FY25. Uptake of iodide complex FY26 at 37 °C was 17× faster than that of chloride complex FY25, and efflux 1.4× faster. Osmium accumulation decreased markedly after 24 h of exposure. Modelling revealed that this phenomenon could be explained by complex-induced reduction of osmium uptake, rather than by a model involving enhanced osmium efflux. The intracellular osmium concentration threshold above which reduction in drug uptake was triggered was estimated as 20.8 μM (95% confidence interval [16.5, 30]). These studies provide important new insight into the dynamics of transport of this organometallic anticancer drug candidate.

Harnessing the therapeutic potential of anticancer drugs through amorphous solid dispersions

The treatment of cancer is still a major challenge. But tremendous progress in anticancer drug discovery and development has occurred in the last few decades. However, this progress has resulted in few effective oncology products due to challenges associated with anticancer drug delivery. Oral administration is the most preferred route for anticancer drug delivery, but the majority of anticancer drugs currently in product pipelines and the majority of those that have been commercially approved have inherently poor water solubility, and this cannot be mitigated without compromising their potency and stability. The poor water solubility of anticancer drugs, in conjunction with other factors, leads to suboptimal pharmacokinetic performance. Thus, these drugs have limited efficacy and safety when administered orally. The amorphous solid dispersion (ASD) is a promising formulation technology that primarily enhances the aqueous solubility of poorly water-soluble drugs. In this review, we discuss the challenges associated with the oral administration of anticancer drugs and the use of ASD technology in alleviating these challenges. We emphasize the ability of ASDs to improve not only the pharmacokinetics of poorly water-soluble anticancer drugs, but also their efficacy and safety. The goal of this paper is to rationalize the application of ASD technology in the formulation of anticancer drugs, thereby creating superior oncology products that lead to improved therapeutic outcomes.

Phytomedicine polypharmacology: Cancer therapy through modulating the tumor microenvironment and oxylipin dynamics

Integrative approaches in cancer therapy have recently been extended beyond the induction of cytotoxicity to controlling the tumor microenvironment and modulating inflammatory cascades and pathways such as lipid mediator biosynthesis and their dynamics. Profiling of important lipid messengers, such as oxylipins, produced as part of the physiological response to pharmacological stimuli, provides a unique opportunity to explore drug pharmacology and the possibilities for molecular management of cancer physiopathology. Whereas single targeted chemotherapeutic drugs commonly lack efficacy and invoke drug resistance and/or adverse effects in cancer patients, traditional herbal medicines are seen as bright prospects for treating complex diseases, such as cancers, in a systematic and holistic manner. Understanding the molecular mechanisms of traditional medicine and its bioactive chemical constituents may aid the modernization of herbal remedies and the discovery of novel phytoagents for cancer management. In this review, systems-based polypharmacology and studies to develop multi-target drugs or leads from phytomedicines and their derived natural products that may overcome the problems of current anti-cancer drugs, are proposed and summarized.

Pharmacokinetics of Anti-VEGF Agent Aflibercept in Cancer Predicted by Data-Driven, Molecular-Detailed Model

Mathematical models can support the drug development process by predicting the pharmacokinetic (PK) properties of the drug and optimal dosing regimens. We have developed a pharmacokinetic model that includes a biochemical molecular interaction network linked to a whole-body compartment model. We applied the model to study the PK of the anti-vascular endothelial growth factor (VEGF) cancer therapeutic agent, aflibercept. Clinical data is used to infer model parameters using a Bayesian approach, enabling a quantitative estimation of the contributions of specific transport processes and molecular interactions of the drug that cannot be examined in other PK modeling, and insight into the mechanisms of aflibercept's antiangiogenic action. Additionally, we predict the plasma and tissue concentrations of unbound and VEGF-bound aflibercept. Thus, we present a computational framework that can serve as a valuable tool for drug development efforts.

A call for transparent reporting to optimize the predictive value of preclinical research

The US National Institute of Neurological Disorders and Stroke convened major stakeholders in June 2012 to discuss how to improve the methodological reporting of animal studies in grant applications and publications. The main workshop recommendation is that at a minimum studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. We recognize that achieving a meaningful improvement in the quality of reporting will require a concerted effort by investigators, reviewers, funding agencies and journal editors. Requiring better reporting of animal studies will raise awareness of the importance of rigorous study design to accelerate scientific progress.