What Comes Next for Clinical Trials? Mukhtar Ahmed Says “Personalized Research”

The pharmaceutical industry is entering a new phase of drug development. AI, real-world data (RWD), new modalities, and precision medicine are expanding what researchers can measure, predict, and act on. In a conversation with GeneOnline, Mukhtar Ahmed, CEO of BC Platforms, points to a more fundamental challenge: connecting the right data with the right technology and, most importantly, the right research question. As these connections evolve, he believes clinical trials themselves could change—opening the door to what he calls “personalized research.”

AI and RWD Reshape Pharmaceutical Innovation

For decades, healthcare lagged behind other industries in adopting new technologies. Ahmed believes that dynamic changes as patient needs, care models, and regulatory expectations evolve. Value-based care now places greater emphasis on outcomes and patient needs. Regulators also continue to prioritize safety and better treatment pathways. At the same time, biopharma companies are investing heavily in technology and data. Many are backing startups that apply AI to early drug discovery. These companies use foundation models to identify molecules and assess druggability. They also use predictive models to explore patient outcomes across development and commercialization.

He views this as a shift at the top of the R&D funnel. Traditional laboratory workflows still dominate much of drug development. AI-driven discovery creates a new layer alongside those established approaches. Medical innovation adds another source of disruption. Cell and gene therapies, cancer vaccines, and GLP-1 medicines illustrate that shift. The changes extend beyond drug discovery. Ahmed expects real-world evidence to play a larger role across the development lifecycle. Clinical evidence, real-world data, and synthetic data could increasingly converge. That convergence could influence research from discovery through post-launch healthcare.

AI and digital twins could also accelerate that transition. He sees current applications as an early stage of a larger evolution. Future digital twins could generate datasets tailored to specific research questions. They could also process far larger volumes of information. Ahmed expects advances in computing to expand those capabilities further. Yet technology alone cannot solve the industry’s underlying problems. He repeatedly returned to one principle during the conversation: the quality and relevance of the data determine the value of the resulting insight.

That principle also changes how companies should think about AI. Rather than starting with a model, researchers should begin with the proper research question. They can then determine which data, infrastructure, and analytical approaches fit that question. For Ahmed, this shift could also transform clinical research.

Clinical Trials Could Give Way to “Personalized Research”

Ahmed believes different research approaches converge over time. Traditional clinical trials will remain important for safety and efficacy. However, their design could become increasingly pragmatic and data-driven. He does not expect researchers to simply modify protocols developed decades ago. Instead, the planning process itself could change. Researchers could use richer datasets to define patient populations and research questions earlier. They could also design studies around specific patient profiles or cohorts.

That vision leads Ahmed to a phrase he uses deliberately: “personalized research.”

The concept extends personalized medicine more than treatment decisions. Research itself could become more closely tailored to patient populations and their real-world characteristics. Data access would provide much of the foundation for this transition. Patient consent and engagement would also become more important. Modern patients increasingly interact with healthcare through multiple channels. They may visit physicians, diagnostic centers, pharmacies, social care services, and wellness programs. These interactions create different forms of information about the same individual.

Connecting those fragmented experiences brings value. Longitudinal data could help researchers understand patient journeys across healthcare settings. That approach could generate evidence which the traditional clinical trial environment cannot provide. Regulators are also examining how drug development could evolve. Ahmed points to the FDA and European regulators as part of that wider discussion. Their questions increasingly involve AI, data, privacy, and future clinical development models. This raises a more fundamental question about the future role of clinical trials. He asks whether the industry will still define research through discrete trials. Continuous research programs could eventually become more common.

Such a trend could also redistribute power across the research ecosystem. Hospitals could gain greater research capabilities. Technology companies could play a larger role. Pharmaceutical companies could build deeper internal data science capabilities. CROs may also face pressure to rethink their traditional roles. The future will be shaped by experimentation and convergence, grounded in one principle: the right data, at the right time, for the right question.

The Real Bottleneck: Finding the Right Data for the Right Research Question

Despite the excitement around AI, Ahmed indicates two immediate obstacles. Traditional drug development methods remain deeply embedded in the industry. Regulatory systems also take time to adapt. Patient safety places necessary limits on how quickly those systems can change.

The second challenge concerns data. He argues that companies often approach data backwards. They collect large datasets first and search for applications later. That strategy can produce impressive volumes without producing useful evidence.

The alternative starts with the research hypothesis. Researchers must define the outcome they want to understand. They can then identify the relevant patient populations and data sources. Technology choices should follow those decisions. This approach also challenges the idea of an instantly usable dataset. From his perspective, few datasets can answer a specific research question without further work. Data often sits across hospitals, laboratories, biobanks, and diagnostic centers. Different countries also impose different rules around data access and sovereignty.

That fragmentation creates both technical and governance challenges. Researchers may need data from multiple institutions, while those institutions may face restrictions on moving patient-level data across borders or organizational boundaries. The challenge is to connect those datasets without giving up local control.

Federated analytics offers one possible answer. “BC Platforms is not a data aggregator. We don’t just aggregate data and put it on a shelf,” he said.

In this model, data can remain within its original institution or country while researchers analyze information across connected environments. BC Platforms’ trusted research environment, BC Mosaic, applies this approach to data from electronic medical records, laboratories, and imaging systems. The data can be harmonized into research-ready datasets, which researchers can analyze using existing tools or their own models.

Consider a cancer center with a large patient population and its own clinical data. Researchers could use that data within the institution while also collaborating with research centers elsewhere. Pharmaceutical sponsors could then connect with multiple centers to support studies across different patient populations and geographies.

This approach shifts the role of a data platform. Rather than simply providing datasets, it becomes part of the infrastructure that enables collaborative research. That distinction becomes increasingly important as AI moves deeper into pharmaceutical research. AI models need more than large datasets. They require clinically relevant, harmonized, and well-governed data to produce meaningful insights.

Global Data Networks Expand the Reach of Precision Medicine

BC Platforms’ work in Japan offers a national example of this approach. In collaboration with NTT Precision Medicine, the company is helping build the Japan Precision Medicine Platform® (JPP), a federated environment designed to connect healthcare data across institutions while keeping sensitive information under local control.

Japan also illustrates a broader opportunity for regional data networks. Local institutions can retain control over their data while contributing to wider research ecosystems. Such networks could help extend precision medicine across Asia-Pacific and other markets.

Yet connecting data across borders raises questions of consent, privacy, cybersecurity, and institutional control. Different countries also operate under different regulatory frameworks, making a one-size-fits-all approach difficult. Where direct data sharing proves impractical, synthetic data could offer another route. Ahmed points to digital-twin-derived datasets, alongside de-identification and tokenization, as potential tools.

The same considerations shape what it means for data to be AI-ready. Large patient datasets alone do not guarantee useful results. Researchers also need reliable provenance, clinical relevance, harmonization, and appropriate governance. The choice of AI architecture matters as well, with factors such as validation, human oversight, safeguards, and causal analysis influencing the quality of downstream insights.

These requirements are also changing the role of data platforms. Rather than simply supplying datasets, they can help researchers define protocols, identify suitable data custodians, and build the infrastructure needed to answer specific research questions. For pharmaceutical companies working across fragmented data environments, that support could become increasingly valuable.

The broader shift is already reshaping the real-world data market. Competitive advantage may depend less on the sheer volume of data than on how effectively companies connect research questions, data, technology, and evidence generation.

From Personalized Medicine to “Personalized Research”

Looking five to ten years ahead, Ahmed foresees this convergence to continue. Value-based healthcare, sponsored research, medtech, human health, social services, and wellness could increasingly interact, creating new opportunities to generate evidence across the patient journey. This could also expand the definition of real-world evidence. Researchers may draw on more diverse information, while AI could help connect fragmented datasets and identify patterns that traditional research approaches might miss.

For him, an important condition for that future is that AI must become more responsible as it becomes more powerful.

The challenge is also geographic. Different regions will adopt these technologies at different speeds, and Ahmed sees healthcare equity as one of the industry’s major long-term challenges. Low- and middle-income countries may face particular barriers despite having large patient populations and substantial unmet medical needs.

This creates a critical challenge for global precision medicine. Healthcare cannot become truly personalized if its evidence base excludes large parts of the population. The next phase therefore requires more than collecting data. It requires broader access, better representation, and evidence that remains relevant to the populations being studied.

BC Platforms’ strategic direction reflects this exact vision. As research paradigms evolve, the company continues to redefine its role at the intersection of data, technology, and evidence generation. To Ahmed, the destination comes down to “personalized research”—delivering the right research at the right time for the right patient profile.

AI provides analytical scale, real-world data adds patient context, and federated infrastructure bridges fragmented health systems. However, these tools are merely the means, not the destination. Precision medicine’s true potential lies not just in personalizing treatment, but in ensuring that medical research itself becomes genuinely relevant, representative, and patient-centric.

Read the original article on GeneOnline.