High-Resolution Mutational Profiling Suggests the Genetic Validity of Glioblastoma Patient-Derived Pre-Clinical Models

Recent advances in the ability to efficiently characterize tumor genomes is enabling targeted drug development, which requires rigorous biomarker-based patient selection to increase effectiveness. Consequently, representative DNA biomarkers become equally important in pre-clinical studies. However, it is still unclear how well these markers are maintained between the primary tumor and the patient-derived tumor models. Here, we report the comprehensive identification of somatic coding mutations and copy number aberrations in four glioblastoma (GBM) primary tumors and their matched pre-clinical models: serum-free neurospheres, adherent cell cultures, and mouse xenografts. We developed innovative methods to improve the data quality and allow a strict comparison of matched tumor samples. Our analysis identifies known GBM mutations altering PTEN and TP53 genes, and new actionable mutations such as the loss of PIK3R1, and reveals clear patient-to-patient differences. In contrast, for each patient, we do not observe any significant remodeling of the mutational profile between primary to model tumors and the few discrepancies can be attributed to stochastic errors or differences in sample purity. Similarly, we observe ∼96% primary-to-model concordance in copy number calls in the high-cellularity samples. In contrast to previous reports based on gene expression profiles, we do not observe significant differences at the DNA level between in vitro compared to in vivo models. This study suggests, at a remarkable resolution, the genome-wide conservation of a patient’s tumor genetics in various pre-clinical models, and therefore supports their use for the development and testing of personalized targeted therapies.     

An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients’ tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.     

Bioengineered 3D Human Kidney Tissue,a Platform for the Determination of Nephrotoxicity

The staggering cost of bringing a drug to market coupled with the extremely high failure rate of prospective compounds in early phase clinical trials due to unexpected human toxicity makes it imperative that more relevant human models be developed to better predict drug toxicity. Drug–induced nephrotoxicity remains especially difficult to predict in both pre-clinical and clinical settings and is often undetected until patient hospitalization. Current pre-clinical methods of determining renal toxicity include 2D cell cultures and animal models, both of which are incapable of fully recapitulating the in vivo human response to drugs, contributing to the high failure rate upon clinical trials. We have bioengineered a 3D kidney tissue model using immortalized human renal cortical epithelial cells with kidney functions similar to that found in vivo. These 3D tissues were compared to 2D cells in terms of both acute (3 days) and chronic (2 weeks) toxicity induced by Cisplatin, Gentamicin, and Doxorubicin using both traditional LDH secretion and the pre-clinical biomarkers Kim-1 and NGAL as assessments of toxicity. The 3D tissues were more sensitive to drug-induced toxicity and, unlike the 2D cells, were capable of being used to monitor chronic toxicity due to repeat dosing. The inclusion of this tissue model in drug testing prior to the initiation of phase I clinical trials would allow for better prediction of the nephrotoxic effects of new drugs.     

TREAP: A New Topological Approach to Drug Target Inference

Over 50% of drugs fail in stage 3 clinical trials, many because of a poor understanding of the drug’s mechanisms of action (MoA). A better comprehension of drug MoA will significantly improve research and development (R&D). Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug-induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA’s accuracy. We find that network topology predominantly determines the accuracy of ProTINA’s predictions. We further show that the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. We then demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP, that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous because it is not computationally demanding, provides easy-to-interpret results, and is often more accurate at predicting drug targets than current state-of-the-art approaches.     

Rituximab: How approval history is reflected by a corresponding patent filing strategy

Because drug development is not a static process, a drug’s market authorisation may change over time. In many cases, the number of indications for which a drug is approved increases. Because this facet of drug development also comes at significant costs, a corresponding patent filing strategy is required to protect these investments. The strategy as applied to rituximab, which is approved for a variety of indications, is discussed in this review.     

Considerations and recommendations for assessment of plasma protein binding and drug–drug interactions for siRNA therapeutics

At the time of writing, although siRNA therapeutics are approved for human use, no official regulatory guidance specific to this modality is available. In the absence of guidance, preclinical development for siRNA followed a hybrid of the small molecule and biologics guidance documents. However, siRNA differs significantly from small molecules and protein-based biologics in its physicochemical, absorption, distribution, metabolism and excretion properties, and its mechanism of action. Consequently, certain reports typically included in filing packages for small molecule or biologics may benefit from adaption, or even omission, from an siRNA filing. In this white paper, members of the ‘siRNA working group’ in the IQ Consortium compile a list of reports included in approved siRNA filing packages and discuss the relevance of two in vitro reports—the plasma protein binding evaluation and the drug–drug interaction risk assessment—to support siRNA regulatory filings. Publicly available siRNA approval packages and the literature were systematically reviewed to examine the role of siRNA plasma protein binding and drug–drug interactions in understanding pharmacokinetic/pharmacodynamic relationships, safety and translation. The findings are summarized into two decision trees to help guide industry decide when in vitro siRNA plasma protein binding and drug–drug interaction studies are warranted.     

Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID‐19

Amid the COVID‐19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug–virus association entries from literature by text mining and built a human drug–virus association database. To the best of our knowledge, it is the largest publicly available drug–virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug–virus association network, the drug–drug chemical structure similarity network, and the virus–virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug–virus association is unassociated). A comparison on the curated drug–virus database shows that WRMF performs better than a few state‐of‐the‐art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF''s performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug–virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.     

How to Capitalize on the Retest Effect in Future Trials on Huntington’s Disease

The retest effect—improvement of performance on second exposure to a task—may impede the detection of cognitive decline in clinical trials for neurodegenerative diseases. We assessed the impact of the retest effect in Huntington’s disease trials, and investigated its possible neutralization. We enrolled 54 patients in the Multicentric Intracerebral Grafting in Huntington’s Disease (MIG-HD) trial and 39 in the placebo arm of the Riluzole trial in Huntington’s Disease (RIL-HD). All were assessed with the Unified Huntington’s Disease Rating Scale (UHDRS) plus additional cognitive tasks at baseline (A₁), shortly after baseline (A₂) and one year later (A₃). We used paired t-tests to analyze the retest effect between A₁ and A₂. For each task of the MIG-HD study, we used a stepwise algorithm to design models predictive of patient performance at A₃, which we applied to the RIL-HD trial for external validation. We observed a retest effect in most cognitive tasks. A decline in performance at one year was detected in 3 of the 15 cognitive tasks with A₁ as the baseline, and 9 of the 15 cognitive tasks with A₂ as the baseline. We also included the retest effect in performance modeling and showed that it facilitated performance prediction one year later for 14 of the 15 cognitive tasks. The retest effect may mask cognitive decline in patients with neurodegenerative diseases. The dual baseline can improve clinical trial design, and better prediction should homogenize patient groups, resulting in smaller numbers of participants being required.     

Joint Modeling of the Clinical Progression and of the Biomarkers' Dynamics Using a Mechanistic Model

Summary Joint models are used to rigorously explore the relationship between the dynamics of biomarkers and clinical events. In the context of HIV infection, where the multivariate dynamics of HIV‐RNA and CD4 are complex, a mechanistic approach based on a system of nonlinear differential equations naturally takes into account the correlation between the biomarkers. Using data from a randomized clinical trial comparing dual antiretroviral therapy to a single drug regimen, a full maximum likelihood approach is proposed to explore the relationship between the evolution of the biomarkers and the time to a clinical event. The role of each marker as an independent predictor of disease progression is assessed. We show that the joint dynamics of HIV‐RNA and CD4 captures the effect of antiretroviral treatment; the CD4 dynamics alone is found to capture most but not all of the treatment effect.     

Oral Delivery of Lipophilic Drugs: The Tradeoff between Solubility Increase and Permeability Decrease When Using Cyclodextrin-Based Formulations

The purpose of this study was to investigate the impact of oral cyclodextrin-based formulation on both the apparent solubility and intestinal permeability of lipophilic drugs. The apparent solubility of the lipophilic drug dexamethasone was measured in the presence of various HPβCD levels. The drug’s permeability was measured in the absence vs. presence of HPβCD in the rat intestinal perfusion model, and across Caco-2 cell monolayers. The role of the unstirred water layer (UWL) in dexamethasone’s absorption was studied, and a simplified mass-transport analysis was developed to describe the solubility-permeability interplay. The PAMPA permeability of dexamethasone was measured in the presence of various HPβCD levels, and the correlation with the theoretical predictions was evaluated. While the solubility of dexamethasone was greatly enhanced by the presence of HPβCD (K_1∶1 = 2311 M⁻¹), all experimental models showed that the drug’s permeability was significantly reduced following the cyclodextrin complexation. The UWL was found to have no impact on the absorption of dexamethasone. A mass transport analysis was employed to describe the solubility-permeability interplay. The model enabled excellent quantitative prediction of dexamethasone’s permeability as a function of the HPβCD level. This work demonstrates that when using cyclodextrins in solubility-enabling formulations, a tradeoff exists between solubility increase and permeability decrease that must not be overlooked. This tradeoff was found to be independent of the unstirred water layer. The transport model presented here can aid in striking the appropriate solubility-permeability balance in order to achieve optimal overall absorption.     

Biomarker technology roundup: from discovery to clinical applications, a broad set of tools is required to translate from the lab to the clinic

Biomarkers are being utilized throughout the drug discovery and development process to understand fundamental biological processes and relationships. Specific biomarkers for disease states, prognosis, and response to therapy have been applied to screening tissues and serum, and serve as new tools in the development of therapeutics, to segment the population for specific treatments. The use of specific biomarkers to screen subjects to determine clinical trial eligibility, and for early toxicology studies, holds the potential to decrease drug failure rates in the later phases of the clinical trial process. Traditional research tools have been employed to study the genes, proteins, and metabolites of interest. In addition, new technologies and permutations of existing technologies have been developed particularly for investigation in the preclinical and clinical phases of drug development. More importantly, the transition of a compound from preclinical to clinical is aided by technologies that span both process segments. Identification of biomarkers that can be studied throughout the development process requires technologies that are both feasible and cost-effective for large patient populations.     

Transpapillary Drug Delivery to the Breast

The study was aimed at investigating localized topical drug delivery to the breast via mammary papilla (nipple). 5-fluorouracil (5-FU) and estradiol (EST) were used as model hydrophilic and hydrophobic compounds respectively. Porcine and human nipple were used for in-vitro penetration studies. The removal of keratin plug enhanced the drug transport through the nipple. The drug penetration was significantly higher through the nipple compared to breast skin. The drug’s lipophilicity had a significant influence on drug penetration through nipple. The ducts in the nipple served as a major transport pathway to the underlying breast tissue. Results showed that porcine nipple could be a potential model for human nipple. The topical application of 5-FU on the rat nipple resulted in high drug concentration in the breast and minimal drug levels in plasma and other organs. Overall, the findings from this study demonstrate the feasibility of localized drug delivery to the breast through nipple.     

Interprofessional Collaboration on an Internal Medicine Ward: Role Perceptions and Expectations among Nurses and Residents

Background

Effective interprofessional collaboration requires that team members share common perceptions and expectations of each other''s roles.

Objective

Describe and compare residents’ and nurses’ perceptions and expectations of their own and each other’s professional roles in the context of an Internal Medicine ward.

Methods

A convenience sample of 14 residents and 14 nurses volunteers from the General Internal Medicine Division at the University Hospitals of Geneva, Switzerland, were interviewed to explore their perceptions and expectations of residents’ and nurses’ professional roles, for their own and the other profession. Interviews were analysed using thematic content analysis. The same respondents also filled a questionnaire asking their own intended actions and the expected actions from the other professional in response to 11 clinical scenarios.

Results

Three main themes emerged from the interviews: patient management, clinical reasoning and decision-making processes, and roles in the team. Nurses and residents shared general perceptions about patient management. However, there was a lack of shared perceptions and expectations regarding nurses’ autonomy in patient management, nurses’ participation in the decision-making process, professional interdependence, and residents’ implication in teamwork. Results from the clinical scenarios showed that nurses’ intended actions differed from residents’ expectations mainly regarding autonomy in patient management. Correlation between residents’ expectations and nurses’ intended actions was 0.56 (p = 0.08), while correlation between nurses’ expectations and residents’ intended actions was 0.80 (p<0.001).

Conclusions

There are discordant perceptions and unmet expectations among nurses and residents about each other’s roles, including several aspects related to the decision-making process. Interprofessional education should foster a shared vision of each other’s roles and clarify the boundaries of autonomy of each profession.     

Unraveling the Gordian knot: genetics and the troubled road to effective therapeutics for Alzheimer’s disease

In the late 20th century, identification of the major protein components of amyloid plaques and neurofibrillary tangles provided a window into the molecular pathology of Alzheimer’s disease, ushering in an era of optimism that targeted therapeutics would soon follow. The amyloid-cascade hypothesis took hold very early, supported by discoveries that dominant mutations in APP, PSEN1, and PSEN2 cause the very rare, early-onset, familial forms of the disease. However, in the past decade, a stunning series of failed Phase-3 clinical trials, testing anti-amyloid antibodies or processing-enzyme inhibitors, prompts the question, What went wrong? The FDA’s recent controversial approval of aducanumab, despite widespread concerns about efficacy and safety, only amplifies the question. The assumption that common, late-onset Alzheimer’s is a milder form of familial disease was not adequately questioned. The differential timing of discoveries, including blood–brain–barrier-penetrant tracers for imaging of plaques and tangles, made it easy to focus on amyloid. Furthermore, the neuropathology community initially implemented Alzheimer’s diagnostic criteria based on plaques only. The discovery that MAPT mutations cause frontotemporal dementia with tauopathy made it even easier to overlook the tangles in Alzheimer’s. Many important findings were simply ignored. The accepted mouse models did not predict the human clinical trials data. Given this lack of pharmacological validity, input from geneticists in collaboration with neuroscientists is needed to establish criteria for valid models of Alzheimer’s disease. More generally, scientists using genetic model organisms as whole-animal bioassays can contribute to building the pathogenesis network map of Alzheimer’s disease.     

Metabolism and Excretion of Mood Stabilizers and New Anticonvulsants

1. The mood stabilizers lithium, carbamazepine (CBZ), and valproate (VPA), have differing pharmacokinetics, structures, mechanisms of action, efficacy spectra, and adverse effects. Lithium has a low therapeutic index and is renally excreted and hence has renally-mediated but not hepatically-mediated drug–drug interactions.2. CBZ has multiple problematic drug–drug interactions due to its low therapeutic index, metabolism primarily by a single isoform (CYP3A3/4), active epoxide metabolite, susceptibility to CYP3A3/4 or epoxide hydrolase inhibitors, and ability to induce drug metabolism (via both cytochrome P450 oxidation and conjugation). In contrast, VPA has less prominent neurotoxicity and three principal metabolic pathways, rendering it less susceptible to toxicity due to inhibition of its metabolism. However, VPA can increase plasma concentrations of some drugs by inhibiting metabolism and increase free fractions of certain medications by displacing them from plasma proteins.3. Older anticonvulsants such as phenobarbital and phenytoin induce hepatic metabolism, may produce toxicity due to inhibition of their metabolism, and have not gained general acceptance in the treatment of primary psychiatric disorders.4. The newer anticonvulsants felbamate, lamotrigine, topiramate, and tiagabine have different hepatically-mediated drug–drug interactions, while the renally excreted gabapentin lacks hepatic drug–drug interactions but may have reduced bioavailability at higher doses.5. Investigational anticonvulsants such as oxcarbazepine, vigabatrin, and zonisamide appear to have improved pharmacokinetic profiles compared to older agents.6. Thus, several of the newer anticonvulsants lack the problematic drug-drug interactions seen with older agents, and some may even (based on their mechanisms of action and preliminary preclinical and clinical data) ultimately prove to have novel psychotropic effects.     

Lymphoblast Oxidative Stress Genes as Potential Biomarkers of Disease Severity and Drug Effect in Friedreich's Ataxia

There is no current approved therapy for the ultimately lethal neuro- and cardio-degenerative disease Friedreich''s ataxia (FA). Finding minimally-invasive molecular biomarkers of disease progression and drug effect could support smaller, shorter clinical trials. Since we and others have noted a deficient oxidative stress response in FA, we investigated the expression of 84 genes involved in oxidative stress, signaling, and protection in control and FA lymphoblasts ranging from 460 to 1122 GAA repeats. Several antioxidant genes responded in a dose-dependent manner to frataxin expression at the mRNA and protein levels, which is inversely correlated with disease progression and severity. We tested the effect of experimental Friedreich’s ataxia therapies dimethyl fumarate (DMF) and type 1 histone deacetylase inhibitor (HDACi) on biomarker mRNA expression. We observed that exposure of lymphoblasts to DMF and HDACi dose-dependently unsilenced frataxin expression and restored the potential biomarkers NCF2 and PDLIM1 expression to control levels. We suggest that in addition to frataxin expression, blood lymphoblast levels of NCF2 and PDLIM1 could be useful biomarkers for disease progression and drug effect in future clinical trials of Friedreich’s ataxia.     

Ribavirin for treating Lassa fever: A systematic review of pre-clinical studies and implications for human dosing

Ribavirin is currently the standard of care for treating Lassa fever. However, the human clinical trial data supporting its use suffer from several serious flaws that render the results and conclusions unreliable. We performed a systematic review of available pre-clinical data and human pharmacokinetic data on ribavirin in Lassa. In in-vitro studies, the EC50 of ribavirin ranged from 0.6 μg/ml to 21.72 μg/ml and the EC90 ranged from 1.5 μg/ml to 29 μg/ml. The mean EC50 was 7 μg/ml and the mean EC90 was 15 μg/ml. Human PK data in patients with Lassa fever was sparse and did not allow for estimation of concentration profiles or pharmacokinetic parameters. Pharmacokinetic modelling based on healthy human data suggests that the concentration profiles of current ribavirin regimes only exceed the mean EC50 for less than 20% of the time and the mean EC90 for less than 10% of the time, raising the possibility that the current ribavirin regimens in clinical use are unlikely to reliably achieve serum concentrations required to inhibit Lassa virus replication. The results of this review highlight serious issues with the evidence, which, by today standards, would be unlikely to support the transition of ribavirin from pre-clinical studies to human clinical trials. Additional pre-clinical studies are needed before embarking on expensive and challenging clinical trials of ribavirin in Lassa fever.     

Compositional and genetic alterations in Graves’ disease gut microbiome reveal specific diagnostic biomarkers

Graves’ Disease is the most common organ-specific autoimmune disease and has been linked in small pilot studies to taxonomic markers within the gut microbiome. Important limitations of this work include small sample sizes and low-resolution taxonomic markers. Accordingly, we studied 162 gut microbiomes of mild and severe Graves’ disease (GD) patients and healthy controls. Taxonomic and functional analyses based on metagenome-assembled genomes (MAGs) and MAG-annotated genes, together with predicted metabolic functions and metabolite profiles, revealed a well-defined network of MAGs, genes and clinical indexes separating healthy from GD subjects. A supervised classification model identified a combination of biomarkers including microbial species, MAGs, genes and SNPs, with predictive power superior to models from any single biomarker type (AUC = 0.98). Global, cross-disease multi-cohort analysis of gut microbiomes revealed high specificity of these GD biomarkers, notably discriminating against Parkinson’s Disease, and suggesting that non-invasive stool-based diagnostics will be useful for these diseases.Subject terms: Microbiome, Biomarkers, Population genetics     

Premarket Safety and Efficacy Studies for ADHD Medications in Children

Background

Attention-deficit hyperactivity disorder (ADHD) is a chronic condition and pharmacotherapy is the mainstay of treatment, with a variety of ADHD medications available to patients. However, it is unclear to what extent the long-term safety and efficacy of ADHD drugs have been evaluated prior to their market authorization. We aimed to quantify the number of participants studied and their length of exposure in ADHD drug trials prior to marketing.

Methods

We identified all ADHD medications approved by the Food and Drug Administration (FDA) and extracted data on clinical trials performed by the sponsor and used by the FDA to evaluate the drug’s clinical efficacy and safety. For each ADHD medication, we measured the total number of participants studied and the length of participant exposure and identified any FDA requests for post-marketing trials.

Results

A total of 32 clinical trials were conducted for the approval of 20 ADHD drugs. The median number of participants studied per drug was 75 (IQR 0, 419). Eleven drugs (55%) were approved after <100 participants were studied and 14 (70%) after <300 participants. The median trial length prior to approval was 4 weeks (IQR 2, 9), with 5 (38%) drugs approved after participants were studied <4 weeks and 10 (77%) after <6 months. Six drugs were approved with requests for specific additional post-marketing trials, of which 2 were performed.

Conclusions

Clinical trials conducted for the approval of many ADHD drugs have not been designed to assess rare adverse events or long-term safety and efficacy. While post-marketing studies can fill in some of the gaps, better assurance is needed that the proper trials are conducted either before or after a new medication is approved.     

Properties of Protein Drug Target Classes

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme.