SaMD or Software as a Medical Device can be described as a software constructed to be used in medical devices. These softwares can be run on different operating systems and virtual platforms. Examples of SaMD include:

• standalone softwares running on general computers, smartphones and tablets.
• SaMD that can detect interrupted breathing during sleep by using a microphone of a smart device.
• SaMD that can analyze the heart beat rate.

A software can behave as SaMD when it is run on a medical hardware device, but does not serve any intended purpose for the hardware. However, if the software controls the hardware device or drives it, then it cannot be called SaMD.

Softwares, when used as medical devices have several challenges, the major ones being  clinical evaluation, scientific validity and clinical validation. All these three must be performed with accuracy as they are crucial for a successful launch of  SaMD in the clinical market.

• Clinical evaluation of SaMD is described as a set of ongoing activities which are conducted to assess and analyze the SaMD’s safety, performance and effectiveness.
• Scientific validity/ Valid Clinical Association of SaMD refers to the extent of clinical acceptance of the SaMD’s output in terms of concept, conclusion and measurements. It ensures that the output of SaMD corresponds accurately to the healthcare situations and conditions in the real world.
• Clinical validation can be described as the relationship between verification and validation results of the algorithm used and the clincial condition in question. Clinical validity is determined by the manufacturer of the SaMD while developing it before distribution (pre-market) and when it is in use after distribution (post-market).

The basic programming model of a SaMD is given below.

Figure 1: SaMD Basic Programming Model

Different softwares are used for medical purposes, and they include the following:

1. Advanced analytics – described as a device which can use big and large complex data sets from a variety of sources by identifying and analyzing the data, and uses this data for medical devices.
2. Artificial intelligence – described as a device which mimics human learning and reasoning. Machine learning, neural networks and natural language processing are all different types of AI.
3. Cloud – described as a device which has an internet based computing system, whcih provides with computer processing resources and data. Several resources such as servers, computer networks and storage devices can share the pool of data from the cloud system.
4. Cybersecurity – described as a device which prevents unauthorized access, misuse, modification, or denial of use of any information that can be transferred to an external recipient from a medical device.
5. Interoperability – a device that exchanges and uses information with another medical/non-medical product via an electronic interface.
6. Medical Device Data System (MDDS) – a software (or hardware) used to transfer and store data, convert formats of data and display data of the medical device without the alteration of parameters of any other connected medical device.

Premarket notification is a key element for the regulation of medical devices. There are diverse medical products in the market such as glassware and reagents of laboratories, spectacles, lenses, etc, which are not true medical devices. The system of identifying and classifying generic devices is crucial for a successful regulation of such products. To define safety and effectiveness of all types of medical devices, the factors taken into consideration are:

• patient population in question
• labelling and advertising of the ways to use a medical devise
• balance between benefits and risks to health when using a medical device
• reliability of the medical device

The International Medical Device Regulator’s Forum (IMDRF) seeks to demonstrate safety, effectiveness and performance of SaMD. It suggests that a software used in medical sciences is unique with respect to its connectivity. The manufacturers of softwares related to medical devices are encouraged to use this feature of uniqueness to modify the softwares to suit the real-world performances.

It is important to ensure safety in the new technologies used for medical devices, and the way it is done is illustrated below:

Figure 2: Good quality systems ensure safety of patients

Several opportunities associated with software driven medical sciences are discussed below.

• Techniques of advanced analytics can be used to analyze datasets which normally cannot be analyzed by humans. These include big and complex data sets that can only be analyzed by specialized softwares.
• Advanced analytics can discover new patterns in data.
• A patient’s melanoma can be analyzed by an imaging system through comparison with a repository of past melanoma data such as images, diagnosis and treatment plans.
• A personalised 3D model of the coronary arteries can be created from a standard CT by a software, and the model can predict the impact of blockages on flow of blood through the arteries.
• Algorithms used in imaging systems of artificial intelligence can provide diagnostic information on skin cancer.
• AI used in a smart ECG device can estimate the probable occurrence of acute cardiac ischemia (ACI)
• A mobile colposcope can use images stored in cloud and retrieve it for use in the doctor’s chamber later on.
• Cardiovascular images acquired from magnetic resonance scanners can be analyzed by a software through a cloud-based picture archiving and communication system.
• Data of patients from a pulse oximeter can be used by a designed infusion pump to change the settings of another infusion pump as per requirement.
• A software can be used to adjust the pressure, flow settings and volume of air of a ventilator, and also to collect output data about a patient’s carbon dioxide level.
• Patient data from several devices can be received by a centralized patient monitoring system.
• medical data can be electronically displayed

Given below is the total product life cycle of a medical device – the science cycle and the regulatory cycle:

Figure 3a: Science cycle

Figure 3b: Regulatory Cycle

References:

1. https://www.fda.gov/medical-devices/digital-health/digital-health-criteria
2. http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-170921-samd-n41-clinical-evaluation_1.pdf
3. https://www.ncbi.nlm.nih.gov/books/NBK209791/pdf/Bookshelf_NBK209791.pdf
4. Kramer DB, Tan YT, Sato C, Kesselheim AS. Ensuring medical device effectiveness and safety: a cross–national comparison of approaches to regulation. Food Drug Law J. 2014;69(1):1-23, i. PMID: 24772683; PMCID: PMC4091615

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Brief:

• Cardiovascular disease is a significant health concern worldwide despite having many genomics developments providing valuable new candidates for better biomarkers and novel therapeutic targets. This blog focuses on recent trends in this field of genomic biomarkers.
• DNA microarrays, linkage analysis, genome-wide association studies, and other strategies give significant knowledge about the heart’s metabolic diseases. Consisting of many benefits from these approaches, but one must remain conscious of the importance of phenotyping.
• The integration of new technologies promises the discovery and validation of better biomarkers of the presence of cardio disease, its progression, and the response to treatment in this blog.

Introduction:

Recent advancements in the incidence of the cardio disease continue upward globally, yielding a significant impact on morbidity and mortality in Clinical research organization. The number of patients suffering from CVD (Cardiovascular diseases) mounts, with an ageing demographic. The economic and burdens of these diseases will continue to grow, with tremendous consequences in healthcare systems. The search for better biomarkers of the disease’s presence and progression response to treatment has become a matter of urgency. Luckily, technological advances have facilitated high-throughput assessment and mining of the human genome, proteome, and metabolome. In this context, molecular signatures will be increasingly useful in predicting, diagnosing, and managing heart disease. The discovery and development of biomarkers have benefited from the emergence of high-performance genomic and genetic approaches such as DNA microarrays and single nucleotide polymorphism chips, respectively, in Clinical trial Monitoring Services. The ability to screen large populations for gene expression levels and genetic linkage has shed light on the complex interplay of genetic factors involved in CVD. Molecular signatures identify both in the clinical management of disease and in elucidating the mechanisms involved and provide novel therapeutics for Clinical Biostatistics services.

Analyzing the Gene expression

Rise of technologies such as DNA microarrays and analyses of gene expression in various cardiovascular diseases are significant to the pharmaceutical regulatory consulting services. The amount of transcribed genes and the related mRNAs are detectable by the microarrays to discover correlations of diseases, clinical outcome, disease progression, and therapeutic responses. Microarray analysis comparing the expression of genes in non-failing hearts and failing hearts. In a study, 288 genes identifying as differentially expressed between groups with non-failing and failing hearts. Although none of the genes is currently useful for clinical purposes, these biomarkers can still provide therapeutic benefits into the metabolic dysregulation underlying the disease conditions with the healthcare data analytics services. As described, many of the genes up-regulated in failing hearts, relative to those in the non-failing hearts, connected with fatty acid metabolism, whereas those down-regulated relates to glucose metabolism using clinical study design. It has triggered an investigation into the use of drugs to shift the lipid and glucose metabolic defines in myocardial cells, as a potential treatment for heart failures. Gene expression studies have also highlighted the importance of regulation of lipid metabolism in atherosclerosis. Using mouse models, identified numerous genes involved in lipid metabolism contain expression differentially in cardiovascular diseases such as ischemic and non-ischemic diseases. A single biomarker can provide sensitively and specifically as the therapeutic or diagnostic biomarker for the disease. Biomarkers of the future expect to be multi-marker panels characteristic of the disease processes’ complexity underlying pathophysiology. Only a small quantity of sporadic atherosclerosis results from single-gene defects in lipid metabolic pathways.

Genome-wide association studies

Beyond microarray-based expression studies, genome-wide association studies provide a practical approach to discovering genetic biomarkers. Gene variants, on chromosome 9  and chromosome 4 have connects to increased risk for CVD. The applied genome-wide association scanning found a 58 kb interval on chromosome 9p21 that was consistently associated with coronary heart disease in six independent cohorts, from more than four white populations. Another similar finding found an association between a similar region on chromosome 9p21 and coronary artery disease. In the study, two sequence variants on chromosome 4q25 present to be strongly connected with a clinical trial fibrillation among the three populations of European descent. In some instances, the risk locus identified via genome-wide association studies contains genes that have yet to be annotated and characterized. It may also be unclear what these genetic variants induce cellular and molecular differences. Indeed, the newly identified susceptibility loci or SNPs require studies to analyze their involvement in CVD pathogenesis and potential therapeutic analysis for testing. Each SNP may likely have a modest influence on the concentrations or function of translated protein products. In contrast, a specific set of SNPs can significantly impact the pathobiology of a particular CVD. Like genes identified in microarray studies, SNPs contain valuable information for CVD mechanisms and potential new therapeutics.

Linkage analysis

Linkage analysis is another approach to finding genetic biomarkers of heart diseases. It permits the identification of infection with DNA markers by examining the patterns of heredity and disease phenotypes among the family members. The linkage analysis looked at families with early-onset coronary artery disease and demonstrated an association between GATQA2, a transcription factor, and susceptibility for coronary artery disease. Recent studies in Coronary Artery Diseases investigations also revealed novel gene candidates, such as LSAMP, a tumour suppressor gene, and KALRN, a gene involved in the Rho GTPase signalling pathway, to be associated with coronary artery disease for therapeutics clinical research.

Wrapping up:

The critical factor to consider when comparing different genetic biomarkers studies is the selection of the patient cohort. It depends on the heterogeneity of the population’s size; the genetic biomarkers detected may be significantly different. Ultimately, the genetic biomarkers obtained using various technologies will be complementation. Future systems in biology studies shed light in this regard and provide a complete picture of each CVD’s genetic mechanisms. Genetic biomarkers may also help uncover, the human genome contains more than 22 000 genes. These current trends of genetic biomarkers are listed in this blog.

References:

1. McManus, Bruce. “Trends in genomic biomarkers.” Heart Metab43 (2009): 19-21.
2. Radpour, R., Barekati, Z., Kohler, C., Holzgreve, W., & Zhong, X. Y. (2009). New trends in molecular biomarker discovery for breast cancer. Genetic Testing and Molecular Biomarkers, 13(5), 565-571.
3. Srinivas, P. R., Kramer, B. S., & Srivastava, S. (2001). Trends in biomarker research for cancer detection. The lancet oncology, 2(11), 698-704.

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• Medical writers must be subject matter experts of the regulatory control inside and out and know the drug under study in clinical research.
• To prepare a successful NDA(New drug application), a medical writer should know all the regulatory submissions basics.
• Pepgra provides you with essential tips for a medical writer to prepare a successful NDA and provides regulatory writing services.

Introduction:

Medical writing includes writing scientific documents of different types, with regulatory and research-related papers, disease or drug-related and promotional literature, publication articles like journals, manuscripts and abstracts, essential content for healthcare sites, health-related publications or news articles. The scientific data in these documents need to be accessible to suit the target audience’s level of understanding, namely, patients or the general public, physicians or the regulators. Medical writers require an understanding of the medical concepts and terminology, knowledge of relevant guidelines regarding the structure and contents of authentic documents, and good writing skills. They also need to be familiar with searching medical literature, understanding and presenting research data, document reviewing, and editing and publishing requirements. Many resources are now accessible for medical writers to regularly get vital training in medical writing science and art and regularly upgrade their knowledge and skills. The demand for the medical report is growing gradually in pharmaceutical and healthcare communication marketplace. Medical writers can work individually or active as full-time professionals.

Tips for a medical writer to prepare a successful NDA

It’s never too early to start your NDA preparation.

It would help if you began to think about your eventual NDA( New Drug Application) from the moment you start planning for your Pre-IND meeting and IND submission, and well before you have dosed a single patient. For instance:

• What is the most suitable regulatory path?
• What are clinical trials needed for the NDA and product label to be supported?
• Do you plan to seek expedited growth and expedited review?
• What is production information needed for the NDA?
• To help your application, do you need long-term toxicology/carcinogenicity studies?
• What needs to be outsourced?

All these questions should be front and centre as early as possible in your mind. Even though your NDA submission seems far off shortly, early preparation will make the difference between smooth sailing and rough seas.

Start assembling your NDA

Far too often, sponsors wait to start compiling their NDA until late in production, which can place undue pressure on resources and timelines. Note, each piece of the NDA you can finish and “put on the shelf” is one bit less that you have to think about later. You can relax a little better as the submission date approaches by knowing what elements can be accomplished early and then proactively completing those assignments during medical writing in clinical trials.

In most cases, by the time the drug reaches the clinic, a significant portion of the nonclinical program will be completed. All of this information can be explicitly pulled into the NDA’s related modules and incorporated as other studies become final. On the clinical side, as soon as you can, you should start reviewing your data and preparing your clinical research reports (CSRs). Before the research is complete, you can even start on the report shell. You can then collect the rest of the components and collect all the required approval signatures until final data is available. Analyses have been carried out using regulatory medical writing in clinical trials. When the time comes to colonize the NDA clinical overview pages, completing these activities would also encourage performance.

Know the related laws and regulatory guidelines

The FDA review division must manage your application, and for NDAs in general, to understand the applicable laws and regulatory guidelines that relate to your specific program. Although rules are legally binding, documents of advice are not in regulatory writing. However, guidance documents include the regulators’ latest thinking, reviewing the NDA (and hopefully approve it). Therefore, it should be done with caution, reason, and consultation with the FDA’s relevant review division to deviate from these guidelines.

Use standardized templates

When you start writing your IND, make sure you use uniform models and that a standard style guide regulates all authorship. To put together a masterful submission, you don’t have to have a fancy eCTD template kit, but you can make sure your submission documents conform to a standard format and style wherever possible. The same grammatical, punctuation, numbering, abbreviation, and text conventions should be practical by all writers working on the submission and should understand how to preserve the dignity of the underlying document’s formatting and styles.

Because of all the data and scientific weight in the submission, this may seem like a minor detail. Still, a polished, well-written, regularly formatted application package that conforms to current regulatory document requirements goes a long way to the success of a submission.

Enter your study data

To get all your early-phase data entered, queried, cleaned, and locked, do not wait until Phase 3. It will save many headaches, later on, to get an early start on data management and free up time to emphasis on other tasks as the NDA submission date approaches.  If the data is locked, you can start producing study-specific tables, listings, and figures to help study reports and summaries inside the NDA.

Request a Pre-NDA meeting

Your primary late-stage opportunity to get feedback from the FDA on your request is the Pre-NDA meeting. As such, you must put a lot of thought into the topics of discussion you want to raise and ensure that the questions you pose are straightforward, well-supported, and placed to allow the FDA to make a meaningful response. Suppose any particular elements of your program or submission may affect successful filing or review and have not been addressed through previous interactions. In that case, it is now time for the FDA to discuss them. Prepare your claims and your “Plan B” well in advance and consider the possible impacts of both of these on your submission’s content and deadlines from medical writing solutions.

Establish realistic timelines for every aspect

Communicate deadlines and obligations, and follow-up often, to all relevant stakeholders and participants. The overwhelming amount of information that needs to be obtained, processed, interpreted, and shared inside the NDA should be careful in the timelines. Establishing an NDA “tracker” is one of the easiest ways to do this. The most important thing if the tracker is part of a sophisticated software package specifically designed for regulatory submissions, a simple spreadsheet, must contain some crucial and actionable submission information. It requires listing constituent documents with sufficient granularity, the parties involved, each document’s current status, and the due dates.

Allow at least a month for publishing your submission

Publishing timelines are often looking at as something that can be dense if “more critical” tasks such as authoring or scientific review take longer than planned. However, it can sometimes cut to publish timelines, mainly if study reports and different NDA parts are being firm up as you go. It is important not to underrate the vast amount of effort and time involved in setting up a proper submission. Even with leading-edge technologies, publishing needs a considerable amount of time, not to mention quality control activities and final validation of the request, to ensure consistent document formatting, hyperlinking, bookmarking, and arrangement within the eCTD system. Publishing timelines should be compressed with care, prospectively wherever possible, and in close contact with the publishing team at all times.

Take the big picture to guarantee a good submission

Take a minute to consider the picture of what it takes to confirm a good proposal, whether this is your first NDA or just one of many that you’ve done. Pulling an NDA together is a vast undertaking that takes considerable time and effort to complete an investment. Consider your organization or institution’s resources and skills and the internal ability to execute the necessary tasks in your desired timelines, and then make a reasonable assessment of whether additional support is required.

Conclusion:

Pepgra offers several scalable services to help our clients yield high-quality, compliant NDA submissions. Suppose you are thinking of outsourcing your whole NDA(New Drug Application). In that case, you need a partner to assist with publishing and submitting. Even if you have just one or two papers that may benefit from an accomplished clinical pharmacology expert’s examination, Pepgra will help regardless of your need along with Regulatory Writing Service in Clinical Research.

References:

1. Lies, R. L. (1984). Preparing the Nda Summary: How to, How Not to and why. Clinical Research Practices and Drug Regulatory Affairs, 2(3), 259-271.
2. Schwarz, S. W., Dick, D., VanBrocklin, H. F., & Hoffman, J. M. (2014). Regulatory requirements for PET drug production. Journal of Nuclear Medicine, 55(7), 1132-1137.

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