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Published on: May 31, 2021
1 minute read
By: Dr Rajan Choudhary

REGN-COV2 – Artificial "antibody Cocktail"

REGN-COV2 – Artificial "antibody cocktail"

Recently a new drug was approved for use in COVID patients in India. Known as REGN-COV2, this new therapy is based on monoclonal antibodies targeting the SARS-CoV-2 virus responsible for COVID. So how does it work? And is there any evidence behind the treatment?

Monoclonal antibodies are made from components of the human immune system. When the human body recognizes a bacteria or virus in the body, the immune system recognizes and processes proteins on the surface of that bacteria or virus. This is presented to the adaptive immune system, and antibodies have produced that lock onto these surface proteins, neutralizing their function and also tagging them for destruction by other white blood cells.

If we identify antibodies capable of efficiently tagging and neutralizing a certain virus, we can identify the cell capable of producing that antibody, clone it and produce it in vast quantities. The antibodies produced from this cloned cell line will be able to target the virus at the same efficiency and have the same ability to neutralize the virus. Just like with vaccines, there is a risk of mutations developing, resulting in changes in the surface protein that reduce binding and neutralizing effects of the antibodies, allowing them to escape and continue to cause disease

REGN-COV2 uses two neutralizing antibodies (Casirivimab/imdevimab), in an effort to minimize the ability of a mutant virus to develop resistance against the antibody cocktail, as the virus would need to develop mutations canceling both antibodies out at once. These antibodies neutralize the receptor-binding domain of SARS-CoV-2 spike protein, preventing viral entry through ACE2 receptors.

In a study published in the New England Journal of Medicine (one of the most prestigious journals in the medical field) data from 275 patients showed a drastic reduction in viral load in patients treated with REGN-COV2. This is clinically significant as hospitalized patients with severe symptoms have higher viral counts than non-hospitalized or asymptomatic patients, suggesting the hypoxaemic, shortness of breath symptoms are related to the viral count and resulting exaggerated immune response.

Based off this result the European Medicines Agency approved the use of REGN-COV2 antibody cocktail in patients who do not require supplemental oxygen but are at high risk of progressing to severe COVID-19.

FDA regulations currently recommend it for patients who are COVID positive with risk factors that may result in their hospitalization but do not recommend it for patients who are already hospitalized, requiring O2 therapy because of COVID or need chronic O2 therapy.

Those that fall into the high-risk categories include patients with a high BMI (over 35), diabetes, on immunosuppressive treatment or with an immunosuppressive disease, over 65 years of age or those with chronic diseases such as renal disease, cardiovascular disease, COPD, or other developmental disorders.

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How Artificial Intelligence Is Transforming Patient Care in India

As a clinician working closely with patients across urban clinics and remote teleconsultation setups, I have seen firsthand how delayed diagnosis, fragmented follow-up, and specialist shortages affect outcomes in India. Artificial intelligence is not a futuristic concept in Indian healthcare anymore. It is actively reshaping how we diagnose diseases, monitor patients, and prevent complications.

AI, when used responsibly under clinical supervision, is becoming a critical support system for doctors and a powerful safety net for patients navigating a complex healthcare ecosystem.

Why India’s Healthcare System Needs AI

India’s healthcare challenges are deeply structural. A large population burdened by lifestyle diseases, combined with uneven access to medical expertise, creates gaps that traditional systems struggle to bridge.

In daily practice, we increasingly see patients presenting late with diabetes, hypertension, heart disease, or cancer. Many ask a simple but important question: why was this not detected earlier? The answer often lies in limited screening, overloaded clinicians, and lack of continuous monitoring.

Chronic conditions dominating Indian clinics today include:

Diabetes affecting over 100 million individuals.
Hypertension rising even among young adults.
Cardiovascular disease driven by late detection.
Increasing cancer incidence with delayed diagnosis.

AI matters here because it supports earlier identification of risk patterns, reduces diagnostic delays, and allows clinicians to focus on decision-making rather than data overload.

How AI Is Changing Medical Diagnosis

One common concern patients raise during consultations is whether AI can truly diagnose diseases accurately. In practice, AI does not replace a doctor. It acts as a high-speed analytical assistant.

AI in Imaging and Diagnostics

AI systems can rapidly analyse:

X-rays and CT scans.
MRI images.
Mammograms.
Pathology slides.
Cardiac and neurological imaging.

These tools flag abnormalities within seconds, allowing doctors to prioritise critical findings. Clinical studies published in peer-reviewed journals have shown that AI models can match specialist-level accuracy for specific imaging tasks when used correctly.

From a physician’s perspective, the real benefit is not speed alone. It is consistency. AI reduces the risk of missed findings during high-volume diagnostic workflows, especially in resource-constrained settings.

Can AI Monitor Patients Outside Hospitals

Patients managing chronic illness often ask whether technology can help them avoid repeated hospital visits. AI-enabled remote monitoring is one of the most meaningful advances in this area.

AI-Supported Remote Patient Monitoring

AI continuously evaluates trends in:

Blood pressure.
Heart rate variability.
Blood glucose patterns.
Oxygen saturation.
Physical activity and sleep quality.

Rather than reacting to a single abnormal value, AI identifies worsening trends over time. Clinically, this allows early intervention before complications escalate.

Evidence from global health system studies shows that continuous monitoring can significantly reduce avoidable hospital admissions, particularly for diabetes, heart disease, and elderly patients.

Using AI to Predict and Prevent Chronic Diseases

Preventive healthcare remains underdeveloped in India. Most patients seek care after symptoms appear. AI helps shift this model.

By analysing medical history, lifestyle habits, vitals, and environmental factors, predictive models can estimate:

Future heart attack risk.
Progression of diabetes.
Decline in kidney function.
Stroke probability.
Asthma exacerbation triggers.

Patients often ask if AI can really prevent disease. Prevention here means early warnings. When risk patterns are detected early, doctors can adjust treatment plans, recommend lifestyle changes, and prevent irreversible damage.

Personalised Treatment in a Diverse Indian Population

Indian patients differ widely in genetics, diet, stress patterns, and cultural habits. Standardised treatment protocols often fall short.

AI supports personalised care by analysing:

Medication responses.
Dietary intake.
Blood markers.
Sleep and stress trends.
Coexisting medical conditions.

For example:

In diabetes care, AI helps personalise carbohydrate distribution and medication timing.
In hypertension, it identifies sodium sensitivity and stress-related spikes.
In hormonal conditions like PCOS, it aligns nutrition and activity with cycle patterns.

From a clinical standpoint, personalised insights improve adherence and reduce relapse rates.

AI-Enabled Telemedicine and Smarter Consultations

Telemedicine has become an essential part of care delivery in India. Patients frequently ask whether online consultations are as effective as in-person visits.

AI enhances telemedicine by:

Structuring symptom inputs before consultations.
Routing patients to the appropriate specialist.
Generating concise medical summaries for doctors.
Supporting follow-up reminders and medication adherence checks.

When used correctly, AI reduces diagnostic delays and improves consultation efficiency without compromising safety.

Expanding Healthcare Access Beyond Cities

A major question in public health is whether AI can truly improve rural healthcare access. In practice, it already is.

AI enables:

Remote diagnostics supported by portable devices.
Virtual specialist consultations for rural clinics.
Smartphone-based imaging and screening tools.
AI-guided triage in underserved regions.

By reducing dependence on physical proximity to specialists, AI helps bridge longstanding geographical barriers in India’s healthcare system.

Safety, Ethics, and the Role of Doctors in AI Care

Patients rightly express concern about safety, privacy, and over-reliance on technology. These concerns are valid.

Responsible AI use in healthcare requires:

Transparent algorithms.
Explicit patient consent.
High-quality, verified medical datasets.
Strict data privacy safeguards.
Continuous clinical supervision.

In ethical practice, AI outputs never replace medical judgment. Doctors remain accountable for decisions. Human-in-the-loop verification is essential to ensure patient safety and trust.

What This Transformation Means for Indian Patients

Artificial intelligence is fundamentally changing patient care in India by making healthcare more proactive, more precise, and more accessible. From early diagnosis to personalised treatment and continuous monitoring, AI empowers both patients and clinicians with data-backed clarity.

SecondMedic’s patient-first approach integrates AI as a clinical support system, not a replacement for doctors. By combining medical expertise with digital intelligence, the goal remains simple: better outcomes, earlier intervention, and care that adapts to each patient’s real-world needs.

As clinicians, our responsibility is to ensure that technology serves patients ethically and effectively. When used with care and oversight, AI has the potential to redefine healthcare delivery across India in a way that is inclusive, preventive, and sustainable.

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