The development of a ‘Findable, Accessible, Interoperable and Reusable’ (FAIR) data approach has been an exciting step towards an information-centric approach for the pharmaceutical industry. The potential benefits of this alternative approach to data management and accessibility could significantly support pharmaceutical R&D and drug development. However, adopting this new approach could be challenging for some organisations and may not be as popular as anticipated. 

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Introduction to FAIR data

Two of the main problems limiting drug development and clinical research are the accessibility and management of data. There are vast genetic databases, medical records and scientific archives which have the potential to support the pharmaceutical industry but have only just begun to be utilised efficiently.

FAIR data is a recently developed concept built on a number of principles which aim to support drug discovery through good data management, which are as follows:

• Findable – Data are richly described by metadata and have a unique and persistent identifier

• Accessible – Data and corresponding metadata are understandable to humans and machines, and accessible through defined protocols

• Interoperable – Data and corresponding metadata use formal and accessible knowledge representation to guarantee reuse

• Reusable – Metadata accurately describe the provenance and usage license for the data

Findable is one of the most important principles for FAIR data and is built on the premise that data must be of high quality when it is inputted into the database. In other words, up until its most recent update, data must be meta-tagged to ensure the record of the data is up-to-date and accurate. This includes “information on the author, those that have access to the data, who has modified it and a whole series of characteristics such as category, security classification and file type”. 

This metadata is critical for intelligent databases to filter through vast datasets to retrieve relevant records.

The ultimate goal of FAIR data is to help lower R&D costs, reduce drug development timelines and prevent late-stage failures. Extensive analysis of data across multiple platforms could help researchers to better understand a disease and potentially modify preclinical models to better assess drug pharmacokinetics in a biological system. 

Challenges

The principles for FAIR data helps users to get the most out of the available data, however implementation in the pharmaceutical industry has been slow for two main reasons, according to a publication.

Firstly, the implementation of FAIR data is not necessarily pre-determined or standardised. FAIRification is a long-term solution of how data is created and used within an organisation, however this concept continues to change as our knowledge of processes (biological and digital) evolves. 

This was a point emphasised in the aforementioned publication which states that a critical part of FAIRification is the mapping of data according to a semantic model. “This model describes the meaning and relevance of each data point, and its relationships to other data in the context of a knowledge domain. Gaps in our understanding of biology make this mapping a moving target. New information fills in what we don’t know and changes what we thought we did know.”

The second reason contributing to slow implementation is the potential cultural implications. For those in the pharmaceutical industry who have always worked in information silos, shifting to sharing and reusing data may be considered counterintuitive. Hence, adopting the FAIR principles may be a challenging and arduous process for many organisations. 

Other challenges raised include the need for “clear data licensing, how to proceed and what license to suggest/apply to data from a wide variety of sources (governments, agencies, states, universities, investigators, etc)” and how the need for user identification for access to platforms may introduce a new barrier to data access. 

Importance of FAIR data

The implementation of FAIRification across the pharma industry has potential to support everything from R&D to drug development. According to a recent Nature article, the following stakeholders are those who could benefit the most:

• Researchers wanting to share, get credit, and reuse each other’s data and interpretations

• Professional data publishers offering their services

• Software and tool-builders providing data analysis and processing services such as reusable workflows

• Funding agencies (private and public) increasingly concerned with long-term data stewardship

• Data science community mining, integrating and analysing new and existing data to advance discovery. 

Improving the quality, accessibility and management of data sources across the industry will become a valuable resource for many organisations. Not only could FAIR data be used to address the challenges of long development timelines, but also prevent problems arising in the future, thanks to the shared knowledge of others. 

Drug development relies so heavily on participant information and the findings from previous trials which emphasises the importance of re-usable data to significantly cut down the cost and time of R&D, ultimately shortening the time to market for new and improved drugs. 

The concept of reusing and improving accessibility to shared data would also allow pharma organisations to respond more rapidly and effectively to changes in business demands – in comparison to information silos and generating new data which is time consuming and costly. 

Example of FAIRness 

Open PHACTS is a data integration platform for information pertaining to drug discovery. Using a machine-accessible interface, users can access the platform which provides human and machine-readable representations.  

One of many uses for Open PHACTS is obtaining gene names correlated with UniProt identifiers. UniProt is a freely accessible, online database of protein sequences and functional information. The related proteins retrieved from these methods may represent splice variants, orthologues or homologous paralogues.

The platform draws together multiple sources of publicly-available biomolecular, pharmacological and physicochemical data which can respond to “structured, well defined queries in a meaningful and reproducible way”. 

Charlotte Di Salvo, Lead Medical Writer
PharmaFeatures

For more articles covering the pharmaceutical industry, clinical research and academia, visit our content site PharmaFeatures.

The post Building a FAIR Culture in Pharma appeared first on Proventa International.

Hear from some of the industry leaders including David Cook, Chief Scientific Officer, Blueberry Therapeutics who will be providing his expertise in leading a discussion on using translational research techniques to improve drug development.

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com..

In the context of biology and medicine, nanotechnology encompasses materials, devices, and systems whose structure and function are relevant for small length scales, from nanometers (10⁻⁹ m) through microns. 

Nanotechnology offers a number of advantages for biological research. In comparison to traditional molecular assays, nanoscale devices can differentiate at the level of single molecules and single cells. This great sensitivity can be used to characterise single-cell heterogeneity at extremely high throughput, revealing distinct hierarchies and subpopulations. This is owed to the microscopic structure of nanotechnologies which are typically comparable in size with biomolecules. As a result, they can travel more freely through the human body in comparison to larger molecules.

In addition to research, nanotechnology has contributed to a number of innovations across the drug development process including drug design and delivery. Nanotechnology is also evolving to deliver therapeutic agents to target sites.

Nanomedicine 

Drug delivery systems 

Polymeric nanomaterials are the ideal choice for efficient drug delivery. Polymeric refers to a large molecule made of smaller subunits. The high compatibility and biodegradability are a few of the properties that make polymeric nanomaterials desirable for delivering drugs that have poor solubility and low absorption rates. Nanospheres and nanocapsules are the two main categories of polymeric nanoparticles used as drug delivery systems. 

Chitosan-based nanomaterials are widely used for continued drug release systems for various types of tissues including eye, intestinal and nasal regions. Chitosan is a naturally occurring by-product from the processing of shellfish.

One of the main advantages of using nano drug delivery systems is their ability to penetrate the tissue system. This facilitates easy uptake of the drug by cells, resulting in an efficient drug delivery at the specific target site. Furthermore, nanostructures stay in the blood circulatory system for a prolonged period and enable the release of drugs as per the specified dose. Therefore, they cause fewer systemic fluctuations, reducing the likelihood of adverse effects. 

The method by which nanostructures deliver drugs is divided into two categories, passive and self-delivery. In passive delivery, drugs are integrated into the nanostructure via the hydrophobic effect. Hydrophobic refers to molecules that are repelled by water. The desired amount of drug will then be released at target sites due to the “low content of the drugs which is encapsulated in a hydrophobic environment”. 

In passive drug delivery the drug carrier is transported systematically, and drawn to the target site by affinity influenced by properties like pH, temperature, molecular site and shape.

In self-delivery, the drugs are instead directly conjugated to the nano carrier. With this approach, the drug dissociates rapidly from the nanostructure, hence the timing of release is crucial to ensure the drug reaches the target site. If released prematurely, the bioactivity and efficacy will be significantly compromised.

With the active approach, drug targeting is facilitated by biological agents known as moieties e.g. antibodies and peptides. These agents are coupled with the nano drug delivery system to act as an anchor to receptor structures within the target site. The targets for drug delivery are primarily membrane-bound proteins including receptors, lipid structures or antigens on the cell surface.

Tissue engineering

In addition to drug development, nanotechnology has been used to create biocompatible scaffolds in the creation of implantable tissues. According to an article, these nanoscale structures have been engineered to resemble a native extracellular matrix which can control the release of drugs. In biology, an extracellular matrix is an intricate 3D network consisting of an array of extracellular macromolecules like proteins and carbohydrates. Also known as hard-tissue engineering, this nanotechnology is a relatively new concept used to engineer skeletal muscle tissue. 

The mimicking of the native extracellular matrix is a crucial part of creating an optimum tissue microenvironment including appropriate mechanical strength, ease of monitoring cellular activities and delivering of bioactive agents require a nanoscale approach. Modified electrospinning is one method in which biological factors are incorporated into nanoscaffolds. 

Gold and titanium nanoparticles have been used to enhance cellular functions like proliferation for bone and cardiac tissue regeneration. Several studies support the utility of gold nanoparticles especially as candidates for bone tissue regeneration. This particular group of nanoparticles have shown to influence osteoclast formation, while providing “protective effects on mitochondrial dysfunction in osteoblastic cells”. Osteoclasts are a type of bone cell that breaks down bone tissue. 

These cells are critical for the maintenance of bone repair, metabolism and remodeling. Mitochondria function is critical for cellular function of which tissue regeneration cannot occur. Hence, protection of this component is a significant part of the success of bone regeneration seen so far. 

Cardiac tissue regeneration has been a key focus for nano tissue engineering for many years. Human myocardium is a type of muscle tissue in the heart which typically fails to regenerate after tissue damage. This results in an insufficient number of cardiomyocyte cells and counteracting of scar tissue formation which leads to abnormal arrhythmia and often heart failure. Hence, this represents an unmet need for nanotechnology. 

Engineered cardiac patches are considered a promising approach for regenerating the infarcted heart. Cardiac cells are implanted within the 3D nanoscaffolds, which provide the structural biochemical microenvironment. The formation of tissue arises from cell-cell and cell-matrix interactions facilitated by the scaffold structure. Once the tissue is engineered into a cardiac patch, it is attached to the scar tissue of the heart by a surgical operation, involving synthetic sutures or staples.

While cardiac patches have shown promising preclinical results, there are a significant number of challenges that need to be addressed before clinical implementation. Firstly, one of the issues with using metallic nanoparticles is the inability of these structures to be naturally broken down. As a result, the longer the period the structure remains in the body, the greater the likelihood of cytotoxic events. Secondly, the method by which the cardiac patches have been attached is using staples/sutures during open heart surgery. This is, of course, a highly invasive and risky process to which a less invasive alternative attachment method needs to be developed to reduce the risk of complications. 3D printing has been suggested as a recommendation for patch fabrication. 

To be able to recapitulate the tissue microenvironment and functionality so well through nanotechnology represents a significant step forward in the successful regeneration of tissue. Despite significant setbacks with preclinical testing of cardiac tissue regeneration, research is ongoing to develop novel nano fabrications and less invasive delivery methods. Nanotechnology continues to play an important role across many therapeutic areas and drug development, and will continue to evolve in the next few years to solve current challenges in the field.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Bioinformatics Strategy Meeting, set for 1 July 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Emerging Therapeutics in Translational Medicine: Nanotechnology appeared first on Proventa International.

Omics technologies have proven to be useful molecular tools across many therapeutic areas in facilitating qualitative and quantitative tissue analysis. Spatially Resolved Transcriptomics (SRT) is an example of -omics technology used in oncology to further investigate how the heterogeneity of the tumour microenvironment works. -Omics technologies like SRT methods continue to develop, with further integration into computational biology. 

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Introduction to -omics

The term -omics refers to a “field of study in biological sciences that ends with -omics, such as genomics, transcriptomics, proteomics, or metabolomics.” To clarify, genetics is a branch of biology which studies genetic function, variability and inheritance. A genome is the object of study within genetics; genomics is a more in depth analysis of genetics “which studies the structure, function, evolution, and mapping of genomes”.

In comparison with traditional biochemical methods, -omics technologies are more efficient and timesaving, used to produce large-scale data. -Omics technologies have important applications in biomedical and pharmaceutical research. These technologies have proven to be useful tools and “enable the exploration of the genome, transcriptome, and proteome more broadly with greater sensitivity and resolution”. 

In the context of pharmaceutical research, -omics technologies can be used to provide solutions across drug development including target discovery and validation, drug toxicity and safety assessment. 

High throughput -omics technologies can facilitate the analysis of vast libraries of molecules including genes and proteins in a very short period of time, which is useful for accelerating data-demanding analyses. In addition, the sensitivity, specificity and accuracy of the technology has enabled the identification of key biomarkers, a key part of molecular diagnostics which has seen -omics technology shift into precision medicine. 

Next-generation sequencing (NGS) is an example of a large-scale DNA sequencing technology which characterises the entire genome for an organism. Whole genome sequencing is an important part of research for genetic-based diseases, allowing the compilation of germline variants increasing the risk for inherited diseases. NGS is a more cost-effective and time-efficient qualitative analysis in comparison to previous techniques, i.e. Sanger sequencing. 

-Omics technology

SRT is defined as a ”diverse set of technologies that encode gene expression measurements with information about where in the sample each observation occurred”. Microdissection-based methods are a complex group of SRT methods in which small sections of tissue or cells of interest are prepped, dissected and studied for cellular morphology and gene expression. Laser capture microdissection involves the staining of a small tissue section of a prepared glass slide to be imaged for morphology, and regions of interest include single cells using a microscope-guided laser. 

In situ barcoding-based methods are another group of SRT techniques which enable spatial RNA quantification. One particular technique, Spatial Transcriptomics (ST) initially involves similar prep in which tissue is fixed, stained and then imaged. However the sequence of RNA quantification is inherently more complex involving permeabilisation, reverse transcription and cDNA library formation. The RNA sequence is mapped to specific tissue locations by aligning the histological image acquired at the beginning of the workflow to known spatial barcode locations.

Accurate qualitative analysis of cell morphology has proven the ability of SRT to interrogate tissue at an whole organism ‘-omics scale’. As a result, SRT is becoming an increasingly popular tool within oncology research.

SRT: Precision oncology

As described previously, SRT methods are an important part of qualitatively analysing the spatial variability of genetic expression within a sample. In cancer, there has been substantial evidence supporting spatial variations documented in pathological observations. Hence, SRT has become an important tool in oncology to study the genotypic variation under different tumour microenvironments (TME). 

According to a journal publication, “the spatial association between genetically different cancer cells and blood vessels may be attributed to environmental adaptation, or the ability of cancer cells to modify their environments”. Therefore it is important to understand the spatial variability of the TME in identifying the factors driving tumour heterogeneity. Tumour heterogeneity refers to the variability of tumour cell morphology and phenotypic profiles in addition to differences in metabolism, expression and proliferation.

Previously, microdissection-based methods have been used for decades to study the clonal composition and cell-types of the TME with spatial resolution. Unfortunately, the process is highly labour-intensive and the complexity of dissection limits the throughput of these methods. Solid phase capture-based methods have since become an alternative choice with higher throughput and are suited for translational applications. Solid phase capture-based methods are primarily used to profile composition and spatial heterogeneity of the tumour microenvironment across tumour types. 

In 2020, a publication investigated the spatial analysis of ligand-receptor interactions in skin cancer at genome-wide and single-cell resolution. The team used four complementary SRT technologies to measure gene expression in microarray-based ST-seq techniques (these study cellular interactions and disease mechanisms). A microarray is a tool used to measure the expression of pre-defined genes known as probes. In oncology, this technique has already been applied to prostate cancer and melanoma. 

The results demonstrated that the expression of specific molecules in skin cancer was supported by RNAscope detection assays. In addition, the absolute quantification of the target’s genes through a separate PCR analysis, supported the choice of using an RNAscope assay for quantification. 

Challenges 

Despite their obvious potential for cancer studies, there remain a number of limitations using SRT methods. In the 2020 study, the team highlights how ST-seq still lacks single-cell resolution as well as the read quality that can be captured in each spot which is dependent on the tissue context. 

In order to overcome the limited resolution of ST-sequencing, RNA-ISH is considered a more targeted approach to visualise interactions at single cell level within a region of interest. RNA-ISH, also known as RNA in situ hybridisation, is a “powerful tool to visualize target messenger RNA transcripts in cultured cells, tissue sections or whole-mount preparations”. This technique uses a novel probe design strategy and an amplification system to simultaneously amplify signals and suppress background. Furthermore, in the aforementioned study it is emphasised that there continue to be an absence of a comprehensive drug discovery pipeline studying ligand-receptor interaction from a cancer tissue section. 

SRT methods are also moving towards computational techniques. For example, histological and SRT data has been used to train machine learning algorithms to predict histopathological annotations-based on gene expression data. The computational development of SRT has been observed across life science and the pharmaceutical industry, as noted in a recent Nature article in which a speaker foresees a phase of computational development around finding spatially variable genes or for finding trajectories that leverage the spatial information.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Bioinformatics Strategy Meeting, set for 1 July 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post An Evaluation of -Omics Technology appeared first on Proventa International.

The modernisation of clinical research is an important part of expediting the drug approval process, and this in turn is dependent on the success of clinical trials with a sufficient number of appropriate participants. A number of recent innovations promise to bring about this modernisation: for example, using artificial intelligence (AI) software to research medical databases for eligible participants reduces the time scale for recruitment. Telemedicine will encourage more patients to take part in clinical trials from the comfort of their own home. But as considerable a solution as they are, these new technologies do have their own challenges to face.

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Telemedicine and virtual clinical trials

Telemedicine is the use of remote technology, typically for clinical consultations and the delivery of healthcare. It enables communication between patients and healthcare providers outside the clinical environment. One example of telemedicine is the recording of vital signs during the sleep cycle using actigraphy bracelets, for example Fitbit. 

Telemedicine has facilitated clinical researchers in capturing and analysing data remotely. This method has been desirable for both patients and clinical organisations. Firstly, limited travelling to clinics is more likely to increase patient retention in clinical trials, allowing them to continue day-to-day life. Secondly, the data is more likely to be representative of real-word evidence, in comparison to clinics which do not always show a patient’s true behaviour in an unknown, clinical environment. 

Telemedicine has shown obvious potential in clinical trials for rare diseases. Rare diseases typically have small populations across the entire globe, hence a central site for clinical trials may not be accessible. In March 2017, the European Reference Networks (ERNs) were launched “as virtual networks enabling healthcare providers across Europe to access and share expertise for the care of patients with complex or rare disorders.” 

According to a 2017 publication, the ERN is composed of “at least ten healthcare providers from at least eight different Member States”. In addition to addressing the small number of patients, it enables the liaison of medical experts across the world. This offers an advantage for rare diseases, a therapeutic area which typically lacks knowledge of disease pathology and therapeutic options. 

The increasing number of virtual trials that emerged during the pandemic utilised telemedicine to continue clinical trials, reducing patient-clinician contact in COVID-19 risk assessments. In comparison to traditional clinical trials, patient recruitment, consent and data collection all occur virtually. Face-to-face clinical appointments are often eliminated altogether, along with physical sites. 

The development of virtual trials, also known as decentralised clinical trials, demonstrates the industry evolving rapidly to “deliver approaches that reduce patient burden, increase patient engagement, and promote trial continuity”. The success of virtual clinical trials throughout the pandemic demonstrated the benefits of a patient-centric approach to clinical research. 

One of the challenges to overcome with the digitalisation of clinical trials is the skepticism surrounding data security. The majority of clinical data is highly confidential, with data sources like electronic health records containing a patient’s entire medical history. 

Blockchain is the latest advancement in data security within clinical research. Blockchain technology is a shared system for recording transactions, tracking assets and building trust in a network. The benefit of this is that stored data is less vulnerable to hacking or infringement. Furthermore, the system involves verification steps which ensures the data is protected against unauthorised intervention. 

Artificial intelligence in patient recruitment

Patient recruitment is one of the many areas in clinical trial design streamlined by AI. Natural language processing (NLP) is a branch of AI that enables computers to analyse the written and spoken word. In the context of medicine, it has been used to “allow algorithms to search doctors’ notes and pathology reports for people who would be eligible to participate in a given clinical trial.” In a report by Nature, it is suggested that refinement of NLP could be used in clinical trials to search patient databases for eligible participants. The inclusion and exclusion criteria of clinical trials is typically written in plain text, so shouldn’t require complex algorithms like those required to analyse doctors’ notes.   

In addition to patient recruitment, AI models can also be used to enhance cohort selection. Electronic phenotyping is a well-established discipline which focuses on “reducing population heterogeneity, namely the process of identifying patients with specific characteristics of interest.” Using electronic medical records, “individuals with an explicit observable trait from large quantities of imperfect clinical patient data” can be identified, also known as phenotyping. 

This method is primarily used to reduce patient population heterogeneity rather than enhancing the quality of prognoses. Despite this, phenotyping with EMR data presents a number of challenges including “variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels”.

There are a number of other challenges that exist at present. Firstly, a challenge for the pharmaceutical industry is the lack of personnel to operate AI/ML-based platforms. Furthermore, there is often skepticism about the quality of data generated by AI. Small organisations are often limited in their budget so cannot afford to invest in AI/ML technology. 

Cloud-based software

An optimal clinical trial management system (CTMS) is an important project management tool that supports pharmaceutical and clinical operations. A CTMS maintains contract and payment systems, document management, study milestones and contact management for sites and teams. Legacy CTMS’ are essentially outdated systems within clinical trial management. Recent events highlight the benefits of virtual clinical trials, but current CTMS’ will struggle to manage increasing amounts of data from many different platforms. 

Because of this, the industry is moving towards Cloud-based platforms. Cloud-based applications offer a unified clinical platform to collate documents and data, direct sharing of information with external partners and being able to configure a CTMS system to any type of clinical trial.

Innovations in AI and modern technology are going a long way to address some of the issues of traditional clinical research, including poor patient recruitment. While these innovations do come with their own hurdles to overcome, it is without doubt that in the future they will be refined to improve further.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Clinical Operations Strategy Meeting, set for 15 June 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post The Impact of Modern Technology on Clinical Operations appeared first on Proventa International.

In the last decade, innovations in data technology have greatly contributed to the quality and transparency of clinical research. System security, clinical trial design, and data analysis are some of the areas which have seen significant modernisation to keep up with clinical demand. The following examples are some of the data innovations emerging within clinical research with a possible transition away from traditional methodology. 

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

Blockchain

Blockchain technology is a shared system for recording transactions, tracking assets and building trust in a network. Data is spread across a large network of databases in replica copies, rather than a single store. The benefit of this is that stored data is less vulnerable to hacking or infringement. Furthermore, the system involves verification steps which ensures the data is protected against unauthorised intervention. 

These properties have made blockchaining a desirable asset for clinical research. The storage and processing of sensitive data such as patient medical records and clinical trial data is of paramount importance. As a secure, distributed datastore, blockchain has the potential to provide the data transparency that clinical research needs. A 2019 Frontier article reinforces this point describing how such an approach could help improve the transparency and trustworthiness of clinical trials and benefit the whole clinical research ecosystem”. There are three fundamental properties of blockchain that make it desirable for clinical studies: time-stamping, time-ordering and smart contracts. 

Within the decentralised store of blockchain are ordered records called blocks. Each block is time-stamped and can be updated by a majority of users. The information cannot be erased and the datastore is not ruled by any trusted third party. The benefit of time-stamping emphasises the integrity of data storage which is consistent and secure. In terms of time-ordering, “event consistency allows for checking the integrity of all time-ordered events”. 

Blockchain creates a datastore which is incorruptible and traceable.This prevents “a posteriori reconstruction and allows for securely automating the clinical trial”. These are also known as Smart Contracts. These three core functionalities ensure clinical data is controlled and secure with shareable parameters whether it be for patients or clinical trial stakeholders.

Data mining

According to IBM, data mining is the process of “uncovering patterns and other valuable information from large data sets”. This is also known as knowledge discovery in data (KDD). Data mining techniques can be used to either describe a target dataset (descriptive) or predict outcomes via machine learning (predictive). 

There are four main steps in data mining: “setting objectives, data gathering and preparation, applying data mining algorithms, and evaluating results”. All four steps in sequence enable the mining of data and identification of patterns without prior research and design. 

Data mining is an important tool in clinical research, identifying trends and patterns within stored medical records and follow-up data. The main benefits of data mining in clinical research include searching for potential relationships from medical data to gain effective knowledge of diagnosis and treatment of patients in addition to increasing the accuracy of disease prediction. 

Association analysis is an example of a descriptive data mining technique used to search for frequent patterns or correlations between data within large datasets. It is worth noting that data mining is not expected to replace traditional research methods, but rather as an extension of statistical analysis. 

Outlier detection models signify novelty, anomaly, noise, variation or could be categorised under mining exceptions. In the context of clinical research, this data mining technique could be used to identify human error or negligence in clinical data entry. This is an important part of reducing inaccurate data that could otherwise lead to a misconception of results. In addition to detecting incorrect data, the presence of outliers could lead to novel insights in clinical knowledge discovery.

Disruptive technologies

Beyond these traditional clinical research methods are so-called ‘disruptive innovations’, of which there are many examples seen recently. One of the most obvious is the emerging popularity of telemedicine. Digital health tools like Fitbit watches allow the collection of huge volumes of data that can be accessed remotely by clinicians. These devices are user friendly, allowing subjects to perform as they would day-to-day. The benefit of this enables the collection of raw, real-world data in comparison to clinic visits which may not reflect true behaviour or physiology. 

Mobile applications are frequently being used to report patient outcomes. Patient engagement apps also enable participants to become more involved in clinical trials, with access to documentation which explains complex clinical and scientific terminology. Informed consent can also be collected electronically. These innovations provide patients with the accessibility and transparency they require to feel comfortable throughout clinical trials, potentially improving overall patient retention. Digital health technologies may give trial participants a choice of participating from the convenience of the home rather than traveling to a trial site, which can increase participant engagement and retention.

Decentralised clinical trials (DCTs) offer a more patient-centric approach to clinical research.

Digital health is a key components within decentralised clinical trials which collect data safely and efficiently from the comfort of a patient’s home. The number of virtual clinical trials that emerged during the pandemic allowed clinical research to continue with limited site visits and patient recruitment. In terms of clinical operations, DCTs can reduce the time and financial burden of managing patients at sites across the globe.

In unprecedented circumstances, digital innovations in clinical research enabled clinical trials to continue running in extenuating circumstances. However, the success of virtual clinical trials also demonstrated the benefits of a patient-centric approach to clinical research. Disruptive technologies, data mining and blockchain technologies are a few examples of the digital innovations that have streamlined clinical trials, transitioning from traditional to modernised operations.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Clinical Operations Strategy Meeting, set for 15 June 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Blockchain, Data mining, and Disruptive Technologies: Digital Innovations in Clinical Research appeared first on Proventa International.

Significant recent investment in computational technology has seen a number of new innovations arise in drug discovery – perhaps most notable machine learning (ML). By 2022, it is expected that AI technology will contribute $2.199 billion to pharma’s revenue, with popularity growing across the pharmaceutical industry. Target identification, validation and drug discovery are some of the areas in drug development in which machine learning has shown its potential. 

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

Introduction 

The pipeline from drug discovery to development to approval is a complex and lengthy process. However ML is beginning to show innovations in all stages of drug development. Target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials are some of the opportunities in which ML can be implemented. 

There are two main techniques used to apply ML: supervised and unsupervised learning. Unsupervised learning is a type of algorithm that learns patterns from untagged data. Supervised learning on the other hand, is a type of algorithm formed from labeled training data which consists of a set of training examples.

Supervised learning methods have been used to predict future values of data categories or continuous variables. Unsupervised learning is primarily used for exploratory purposes in the development of models to enable data clustering in a format not specified by the user. This particular technique helps to identify hidden patterns within input data, whereas supervised learning methods predict future outputs based on a trained model of known input and output data. According to a 2020 review, supervised learning techniques such as Support Vector Machines, deep learning and regression methods have already been applied to biomedical challenges in the last decade.

Applications in drug development 

Target identification and validation

The identification and validation of a therapeutic target requires the analysis of vast datasets. Genetic screening and high-content imaging are examples of techniques that produce large datasets that can be exploited for early target identification and validation. However analysis of such data requires appropriate mathematical methods to construct valid statistical models – this is where ML can be exploited. 

As early as 2010, ML was applied in a study for target validation in the form of a “decision tree-based meta classifier”. In this study, the ML platform was proposed as a computational approach to predicting morbid and druggable genes. Morbid genes with mutations are associated with causing hereditary human disease. The tree-based meta-classifier was used to predict targets on a genome-wide scale. It managed to correctly recover “65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%”. The ability of ML to reliably predict specific genes on a genome-wide scale is a huge step forward in further optimising target identification. Prediction of therapeutic targets saves time and resources for pharma companies and potentially utilise the mathematical approach to predict more reliable targets. 

Drug discovery

The Generative Adversarial Network (GAN) is an example of a recent innovation in deep learning for drug discovery. Deep learning is a specialised area of ML that attempts to model abstraction from large-scale data using multi-layered deep neural networks (DNNs). Abstraction is a computer science term that refers to the process of filtering out irrelevant data in order to focus on the desired information. 

As an unsupervised ML method, GAN has proven to address the challenges of supervised ML, primarily the training of large data sets which is often expensive and time-consuming. In a 2017 study, GAN-based frameworks were used to develop and identify novel compounds for anticancer therapy with chemical and biological datasets. 

This study emphasised how the productivity of pharmaceutical research is limited by inefficient early lead discovery processes. It also highlighted how in silico-based approaches like deep learning models can generate reliable data at a reduced cost and time scale relative to current screening methods. 

Computational pathology 

In research, a pathologist interprets the presentation of tissue/cells within a glass slide. The spatial context between cells, size and general cellular structure can be indicators of changes with drug interaction. Computational pathology is becoming an important part of drug development. It has been suggested that this method could allow pharmaceutical companies to discover novel biomarkers and generate them in a more precise, reproducible and high-throughput manner. 

ML allows for high-throughput generation of features for thousands of cells, which is an impossible task for pathologists. Immuno-oncology is a particular therapeutic area which has benefitted from using computational pathology. A 2017 study found that computational analysis of tumour-adjacent benign tissue in prostate cancer revealed information typically ignored by pathologists but has been associated with progression-free survival.

Ongoing challenges in adopting AI/ML

One of the main concerns with ML predictions is overfitting or underfitting. Overfitting is described as a model which consists of “lower quality information/technique but generates higher quality performance. In contrast, underfitting models fail to recognize the data sets’ underlying trend and generalize the new data inputted”. Both errors produce inaccurate results which compromise the reliability of predicted drug targets. Increasing the sample size and cross-validation are often used to address these problems. Cross validation is a technique that uses independent data sets to estimate the accuracy of ML algorithms’ models. 

Another challenge for the pharmaceutical industry is the lack of personnel to operate AI/ML-based platforms. Furthermore, there is often skepticism about the quality of data generated by AI. Small organisations are often limited in their budget so cannot afford to invest in AI/ML technology. 

Despite the improvements needed to refine ML applications, the potential they bring to drug development is significant. In addition to reducing human error, the automation of ML software can analyse data from many sources more accurately and in a shorter period of time. The advancement of AI and ML will continue to reduce the challenges faced by the pharmaceutical industry.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Bioinformatics Strategy Meeting, set for 1 July 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Implementation of Machine Learning in Drug Development appeared first on Proventa International.

Real-world data (RWD) and real-word evidence (RWE) are increasingly used to support drug development and clinical research across life sciences. RWE studies provide insight into the implementation of therapeutic drugs clinical practice based on RWD of the patient population. RWD and RWE have shown significant value in supporting the regulatory decisions in both the drug development process and healthcare settings.

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

What are real-world evidence and real-world data?

As defined by the FDA, RWE is “the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD”. RWD can arise from a variety of sources measuring the status of patient health and/or the delivery of therapeutic care. Electronic health records, disease registries and patient-generated data (in home settings) are examples of platforms that record such data. It is important that RWD is captured in a natural, non-interventional manner rather than clinical trial settings. This ensures that the patient/healthcare data captured is representative of real-life circumstances.

Originally, data collected from randomised controlled clinical trials (RCTs) was considered “higher than that in the real world” according to a 2018 review. Randomisation and double-blind protocols in these trials ensure that comparable cohorts are formed. 

Unfortunately, there are some limitations to RCTs. One obvious limitation is the exclusion and inclusion criteria, which exclude a proportion of patients that may be eligible in the real world. This introduces generalisability, and in turn a level of uncertainty about the efficacy of the drug for two reasons:

• As it is only being tested in a specific group of the patient population, the clinical therapeutic response in other patients could vary greatly  

• Factors such as low compliance and reduced tolerability in the clinical setting may not be truly representative of the drugs “performance” in the real-world. 

As a result, RWE has become an important part of evaluating the efficacy of treatments. In the review, it is suggested that data from RCTs could be supplemented with RWD to bridge the gap between the controlled clinical setting of RCTs and the “harsh realities” of the real world.

RWE and RWD: Drug development

RWD and RWE play an important role in FDA regulatory decisions. According to the FDA article, both RWD and RWE support the organisation in monitoring post-market safety and potential adverse effects of therapeutic drugs and devices.This will have a substantial impact on the drug approval process, by understanding the efficacy of treatment in clinical research and real-world patient care. 

In the FDA article, it was also reiterated that “medical product developers are using RWD and RWE to support clinical trial designs (e.g., large simple trials, pragmatic clinical trials) and observational studies to generate innovative, new treatment approaches.” The importance of RWE studies in drug development is emphasised in an article by IQVIA. It is highlighted that drug developers can use RWE in pre-launch studies as insights in selecting clinical trial endpoints and optimising their recruiting strategies.

In clinical pharmacology, RWD has been useful in solving many issues including the optimisation of dose and regimen. The dose and dose regimen at the time of drug approval should be associated with an acceptable benefit-to-risk profile. In some cases, however, pharmacologists question whether these two factors are optimal i.e., could a higher dose potentially improve therapeutic efficacy without a considerable increase in toxicities. This was a particular point emphasised in a 2019 article reviewing the use of RWD and RWE in drug development. In the article, it is suggested that RWD can be used to identify whether modifications in dosing within the real‐world setting are “performed as recommended in the product labeling, potentially providing information on the clinical outcome (both safety and effectiveness) when clinical practice differs.”

RWE and RWD: Clinical research

These pre-launch (RWE) studies are particularly valuable in the advancement of rare disease therapy. A placebo trial arm may be considered unethical and impractical in the small patient populations of rare diseases. In cases like this, RWE studies can “fill the gaps by providing comparator arms and answering preliminary questions about the treatment journey”.

In oncology clinical trials, due to a limited number of patients it is the common side effects of anticancer drugs that are revealed. The more toxic adverse events however, may be missed due to the limited diversity of patients as a result of restrictive inclusion/exclusion criteria. Limited follow-up duration is also suggested to blame for poor estimation of risk associated with adverse events. 

RWD has been suggested to play an important role in addressing these issues, by providing better characterisation of tolerability and adverse events. An accurate toxicity profile for anticancer drugs is critical to inform physicians and patients about the safety of treatment. This was a key point raised in a 2020 review, which suggested that RWD could be a useful “establishing a definitive analysis of benefits and risks associated with treatment for clinical practice guidelines”.

The healthcare community also uses these data to support decision-making and development of guidelines for use of treatment in clinical practice.

Potential challenges

In comparison to clinical trials, RWD is gathered from a multitude of sources, so it is important that the relevant information is captured. Data access and quality are two of the challenges when using RWD, as highlighted in a 2019 article. The article raises the fact that RWD can come from many types of databases, including pharmacy dispensing data or electronic health records (EHR). The data often varies in quality and can include missing data. Pharmacy dispensing records, for example, are not always the most reliable. Even if a prescription is filled for a patient, there is no data to confirm they are taking as prescribed or even taking a drug at all. Over-the-counter drugs are often not documented in RWD. This introduces a potential confounding factor that can comprise the reliability of RWD.

Integrating data from the numerous RWD sources is also a challenge. Combining data from different platforms including EHRs, digital health devices and genomic imaging is a difficult task. As suggested in the aforementioned article, technological solutions like Natural Language Processing could integrate the data to create a more comprehensive picture and novel insights. 

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Clinical Operations Strategy Meeting, set for 15 June 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Real World Data and Evidence: Emerging Trends Across Clinical Research appeared first on Proventa International.

Data capture and visualization are the key components of clinical trial data analysis. However, with the explosion of COVID-19 around the world, data collection became difficult with poor patient recruitment and a reduction in face-to-face clinics. Advancements in telehealth have enabled clinical trials to collect data virtually, pointing to a potential future for clinical research. 

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

How do clinical trials collect data?

Data capture is the term used to describe how clinical trials collect and manage their data. Electronic data capture (EDC) is a digital platform that enables CROs to collect data securely as well as accelerate the research process. The original paper-based data capture was less secure, and data reusability more difficult in comparison to EDC systems. The storage of clinical trial data within a web-based database offers greater security for sensitive data in comparison to traditional Case Report Forms (CRFs). EDCs vary in their parameters and specifications and according to a 2019 article, the choice of an EDC is typically determined by the following features:

• user-friendly interface, designed to meet the specific needs of medical researchers

• a user-access controlled platform that facilitates collaboration among multiple sites and researchers around the world

• the ability to set data limits for all captured data to reduce human error

• assured compliance with privacy and data protection policies, ensuring that mandatory regulations are met, and that research data is collected and maintained

• seamless integration with existing tools, wearables, legacy systems, and other technology

• the ability to reuse research data for the initial research team, the statistical analysis team, and any future users of the data

• access to proactive support and technical advisors to help set up your clinical trials

However, with data capture comes a huge volume of data, from patients’ medical history to pathology reports to administrative information. Data visualization helps to sort and manage the huge amount of collected data. Typically, data visualization works by converting the data in charts and graphs to enable a comprehensive qualitative and quantitative analysis. Optimized data visualization supports clinicians in recognising trends in patient health and the impact of interventions over time. 

According to a 2017 article, there are three types of interactive data visualizations: demographic data, adverse event data and laboratory data. Interactive data visualization allows clinicians to change the level of data they wish to see, potentially speeding up data analysis. Interaction with the data allows the sharing of detailed insights and presents domains not typically represented graphically e.g., adverse events. 

Successes and challenges of current technologies 

Electronic consent (eConsent)

The ethical requirements and standardized participation information has previously resulted in extensive, complex consent documents. These dense, complicated documents can confuse patients and potentially compromised recruitment for participants put off by the complex consent process. eConsent is a relatively new system which hopes to improve patient engagement. The following features described in a 2020 review offer key advantages over traditional written consent:

• the use of multimedia tools to enhance comprehension

• ready conversion into multiple languages

• a means to track consent in a highly portable manner

• the opportunity to provide information in a more convenient way to persons with an inability to attend clinics

eConsent is a multimedia tool with a multitude of interactive features that enables patients to make informed decisions. Examples of these features include pictures and diagrams, comment boxes, glossaries and a platform for electronic signatures. Gaining a better insight into patient experience promises better cooperation, greater recruitment and improved efficiency of clinical trials. 

There are some obvious flaws, however. In a review of the usability of eConsent, younger users reported greater satisfaction with the technology as a method of informed consent. Older participants on the contrary, were more sceptical and concerned in terms of their privacy and confidentiality of data using mobile technology. Another key issue is that the multimedia components of eConsent may not be accepted in certain countries or sites.  

Telehealth 

Telehealth, also known as telemedicine, is the use of remote technology typically used for clinical consultations and the delivery of healthcare. It allows communication between patients and healthcare providers outside the clinical environment. One example of telemedicine is the monitoring of blood glucose levels in diabetic patients at home. 

Recent events such as the COVID-19 pandemic have highlighted the value of telemedicine in clinical trials. The virtual trials that emerged during the pandemic utilized telemedicine to continue clinical trials whilst reducing patient-clinician contact in risk assessments. 

Remote monitoring and televisits are some examples of telehealth that enabled the continuation of trials in the absence of on-site monitoring and clinic visits. A 2020 article by IQVIA expanded upon the benefits of telemedicine, suggesting that virtual trials will continue to be used in the future. In the article it infers that combining virtual trials with traditional on-site studies (hybrid trials) will “ease patient burden, expand the patient pool, and lower the cost of operation”.

Future innovations 

Limiting clinic visits appears to be the step forward in both healthcare and biopharmaceutical research. Recording data from home not only allows patients to continue their daily routines, but fewer face-to-face visits with clinicians also significantly cuts costs for sponsors. 

Wearable devices have been in the background of clinical research for the last few years, emerging now as a potential method of collecting data from patients outside the clinic. The best-known commercial devices are fitness trackers known as actigraphy bracelets. In healthcare, wrist-worn actigraphy devices are important in measuring vital signs like heart rate, blood pressure and electrodermal activity. This is often used to monitor patients with cardiovascular conditions especially. 

Actigraphy bracelets have shown the potential to improve the data collection for a multitude of diseases, sleep especially. Sleep data collection through studies in sleep labs are not often representative of a patient’s condition. Sleeping in the unknown, clinical environment for long periods of time can impact the natural sleep pattern, producing unreliable results. These bracelets can still record important parameters such as sleep duration but in a more natural environment than sleep clinics.

A 2018 review infers the potential benefits of such devices in early drug development, whereby the “collection of dense physiological data may identify early safety issues and inform dose adjustments and dosing frequencies, or lead to discontinuation of development of certain drug candidates.”

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Clinical Operations Strategy Meeting, set for 15 June 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post An Evaluation of Data Capture and Visualization in Clinical Research appeared first on Proventa International.

AI is by far the biggest buzzword in pharma today – but unlike many other high-profile trends, AI has the potential to rapidly and totally transform the sector forever, speeding up processes, automating basic tasks and making sense of the huge data stockpiles pharma companies are dealing with. Nowhere is this more evident than in R&D, where vast data sets must be stored, formatted and analysed rapidly and the effects of potential treatments must be known.

To clarify the area for professionals who need to know, Proventa International created a White Paper on AI in R&D, looking at the innovations and trends shaping 2020, expert opinion on AI’s impact on R&D, and whether AI can reverse the steady decline in pharma R&D output. Below is just a sample of the full White Paper: to read the larger report, click here.

If you are unable to see the PDF below, please click here.

Joshua Neil, Editor
Proventa International

The post Uses for AI in Pharma R&D appeared first on Proventa International.

Whenever Proventa holds a Strategy Meeting around the world – regardless of meeting topic – the subject of AI comes up. It really is so important in pharma right now, with the potential to solve some of the sector’s big issues. But no-one quite knows where it’ll go next. 

We’re not content to leave these questions unanswered. We looked at a major issue in pharma today – the decline of R&D productivity – to see how AI could help. We spoke to field experts to learn about the problem, what AI is currently doing, and what will change from 2020. 

This is an abridged and shortened version of what we found. The full version of the white paper is available at the bottom of the article. 

If you’d prefer to discover these insights firsthand, join us for our first R&D Strategy Meeting this year in London on 29 and 30 June. 

How Will AI Change R&D in 2020?

Acquisitions

Big pharma companies are starting to build their own internal expertise. There have been few acquisitions of AI startups by big pharma in the past five years, however. This is despite credible demonstrations that AI can accelerate at least a small part of pharma R&D. Until these acquisitions begin, Dr. Alexander Zhavoronkov, CEO of Insilico Medicine, argues that there will be no ‘Year of AI’.

Acquisitions are the easiest means by which new technology and innovation can be brought into a company. According to Dr. Zhavoronkov, executives acquire companies with clinical-stage assets first, despite being perhaps less innovative than other AI companies.  He added that 2019 is the first year where startups are holding credible validation exercises in multiple areas. 

Take-up of AI and ML algorithms in the industry has slowly increased over the last few years. Studies still show, however, that fewer than 5% of healthcare organisations have invested in AI technologies. Despite AI’s use today, only as take-up increases and more companies invest will the technology’s potential be seen.

Shortage of Talent and the Move to Top-Down Skillbases

One of the major difficulties pharma companies face in the coming years is a dearth of industry specialists. More traditional IT and AI companies have acquired many of these. At present, only around 15.6% of AI-driven drug discovery companies’ staff are AI experts.

To combat this lack of expertise and rectify other issues within evolving pharma companies, Dr. Zhavoronkov noted the need for Chiefs of AI to take a more prominent and strategic role in a pharmaceutical company. Often, he said, when companies select their Chief of AI they look for an individual who is embracing AI for the stratification of trials or patient sub-populations, or who excels at text data analysis. What’s more crucial is an AI expert who is able to look at the situation from end to end. They must have the power internally to transform drug discovery processes to incorporate large-scale changes. “You need to put the chief of AI as CEO or CSO of the company.”

A recent study backs up this idea. Only 3% of CEOs and board members in the U.S, Germany and Japan had any experience in both AI and pharma. Their companies were, however, expected to outperform the market due to this knowledge.

Deep Learning

Dr. Peter Henstock, AI & Machine Learning Technical Leader at Pfizer, noted that predictive algorithms have existed in pharma for over 15 years without much change. Deep learning can perform such predictions more accurately – and the technology can be applied to vastly more applications. These include literature and patent mining, image processing, biology and chemistry problems.

Deep Learning consists of a number of hidden layers between input signal and result. Each layer operates independently of its peers but simultaneously. Currently, deep learning is around 10% more accurate at analysing data than the average physician. 

New areas affected include image processing, which is vastly more possible with the AI-granted ability to analyse every single cell on every single slide produced. New algorithms can show details of elements missed by scientists, identify obscure patterns, and determine how individuals are rating the images differently. The same can be said of text, chemical structures and other areas. In all instances new technology allows scientists to do different types of experiment than otherwise they could have.

Deep learning has ramifications across the entirety of the pharma area. With greater analytical and predictive ability, scientists can institute global, large-scale programs to better run R&D. This is changing the nature of pharmaceutical problems that currently cannot be answered.

The Rise of China

Commenting on the companies that were performing the most innovative, forward-thinking work in pharma AI at the moment, Dr. Zhavoronkov said that beyond his own company and certain other biotechs like Deep Genomics, there were a number of innovative companies in China that could not be ignored. 

General statistics buoy up these claims, with Chinese investment in biotech and drug discovery rising sharply in 2019 to $1.4 billion, compared to $125.5 million in 2017. In 2017 the Chinese government released an AI strategic plan, declaring the goal of catching up in the AI race by 2020 and becoming a world leader by 2030. 

China benefits greatly from the size of datasets created from its population, with reduced privacy laws facilitating greater access than is available in some other countries. It has been bolstered by rapid migration of experts from other parts of the world, and governmental policies which push research forward. However, a lack of core pharmaceutical skills and less intellectual property protection will ensure the catch-up is not as swift as it might otherwise be.

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Those are some of the ways AI will change pharma in 2020, but certainly not all: you can find more information on these and other ways in the full R&D white paper, available to download here. 

As always, I look forward to discussing these issues with you throughout the year.

Louis Smikle, CEO
Proventa International

The post R&D: The Impact of AI in 2020 appeared first on Proventa International.