2025 Recap
Despite turmoil in regulatory leadership and uncertainty in capital investment, biotech in 2025 crossed the threshold from promise to proof. AI-discovered drugs swept through to late-stage trials in record time. The first personalized CRISPR therapy was administered to an infant with a rare metabolic disorder. CAR-T therapies expanded beyond cancer into autoimmune diseases with promising early data. As 2026 kicks off, the question shifts from “Can we do it?” to “can we deliver it?” The next year will be defined by the technologies and systems that deliver on their promises, particularly in the clinical space.
1. AI & Machine Learning: From tools to infrastructure
The potential of AI tools rippled through the industry last year. Now, at major conferences like JP Morgan, we’re seeing a major shift from questions about whether AI and machine learning can accelerate drug discovery, clinical trials, and workflows to dedicated efforts to build new infrastructure around AI tools. According to Deloitte’s 2026 Life Sciences Outlook, 78% of biopharma executives expect AI to drive major change in 2026.
Clinical successes like Insilico Medicine’s AI-discovered candidate speeding past the industry average of four years to reaching Phase IIa in under 18 months have set a new stage for AI in drug discovery. In research and discovery, Standford’s CRISPR-GPT is helping researchers design experiments that avoid common failures while compound AI systems work collaboratively across target identification, synthesis planning, and clinical trial design. We expect to see a super bloom in demand for AI-assisted data analysis tools, lab automation, and clinical applications.
2. In Vivo Gene Editing: Single-Dose Cures
Gene editing is transitioning from ex vivo approaches that require extracting and engineering patient cells to in vivo approaches that deliver editing machinery directly to tissues. Baby KJ’s personalized CRISPR therapy in May 2025 proved the concept. By November, Cleveland Clinic published Phase 1 results showing safe cholesterol reduction through direct CRISPR targeting. The shift matters because ex vivo gene therapy requires weeks of specialized manufacturing and costs upward of $500,000 per patient. In vivo approaches could enable single-injection treatments in outpatient settings.
Base editing and prime editing platforms avoid double-strand DNA breaks, improving safety profiles. Lipid nanoparticle delivery systems are getting better at tissue-specific targeting. The technical challenges are being solved. What remains is the production challenge: manufacturing personalized therapeutic guides at the speed patients need them. Companies that crack the manufacturing side will define who wins in gene therapy.
3. Molecular Degraders and Antibody Conjugates
Biotech is moving beyond blocking proteins to eliminating them entirely. PROTACs recruit the cell’s own degradation machinery to destroy target proteins. Antibody-oligonucleotide conjugates reach intracellular targets that traditional antibodies cannot access. As Dr. Catherine Bladen from Vector Labs noted in Biocompare, these technologies fill the gap between antibody targeting and genetic medicine precision.
The production angle here is advances in linker chemistry. Constructs that were once unstable are now reproducible and scalable. Sub-stoichiometric dosing means lower manufacturing volumes for the same therapeutic effect. The druggable proteome is expanding not because targets are suddenly “druggable,” but because mechanism design has improved. Companies that understand linker stability and tissue distribution will outcompete those chasing targets alone.
4. Integrated Platforms & Multi-Omics
Single data types tell incomplete stories. Genomics alone misses what’s happening at the protein level. Proteomics can’t explain why identical mutations produce different outcomes. The industry is moving toward integrated platforms that combine genomics, proteomics, transcriptomics, and spatial imaging into unified patient profiles. Tempus announced multi-year partnerships with NYU Langone Health and Northwestern Medicine at JPM 2026, emphasizing longitudinal molecular profiling throughout treatment journeys. Companies are transitioning from simple data repositories to analytical engines that can identify patterns invisible to single-omic approaches.
The shift matters because AI models need integrated data to deliver on their promise. You can’t train a model to predict immunotherapy response using genomics alone when the answer lies in spatial architecture of the tumor microenvironment combined with proteomic signatures. Element Biosciences announced plans for a new benchtop sequencer upgradable to multi-omic capabilities, signaling that integration is becoming table stakes. Companies building platforms that synthesize multiple data layers into actionable clinical decisions will define the next generation of precision medicine. Success depends on connecting genomics to the other molecular layers that actually determine patient outcomes.
5. Bioprocessing: Breaking the Bottleneck
Without advances in bioprocessing, personalized medicine stays boutique. Orla Cloak, CEO of Minaris Advanced Therapies, put it plainly in a recent interview: the biggest challenge in cell and gene therapy is not the science but scaling to patient demands cost-effectively. Single-use technology is becoming standard for personalized therapies because it offers faster setup, lower contamination risk, and reduced resource consumption. Real-time analytics powered by AI and IoT sensors are providing the visibility needed to manage these flexible systems.
Specific bottlenecks are being addressed with targeted innovations. Viral vector production is improving AAV yields while reducing empty capsids. mRNA vaccine manufacturing timelines have compressed from nine weeks to under four weeks. Pre-formed vesicles allow manufacturers to assemble lipid nanoparticles in advance and load patient-specific payloads later. Bioprocessing is where proof meets production. Every breakthrough mentioned in this article depends on manufacturing advances to reach patients at scale.
6. Strategic Partnerships: Nobody Owns the Full Stack
The complexity of modern biotech means no single company can own the entire value chain anymore. As Melania Palomba of Solvias observed in a recent article from Biocompare, strategic partnerships with chemistry, manufacturing, and controls leaders are no longer optional. They are foundational. Companies that embed CMC expertise early are de-risking development and ensuring they can supply commercial products without interruption.
Technology platform integration is just as critical. AI tools need to connect to lab information systems. Cloud genomics infrastructure needs to support federated learning that enables collaborative model training without sharing proprietary data. Virtual trial platforms are eliminating geographic barriers to participation. Manufacturing partnerships have become competitive advantages. Advanced bioprocessing expertise is separating companies that can scale from companies that cannot. The question is not “make versus buy?” but which partnerships are strategic versus transactional.
The Bridge from Proof to Production
These technologies aren’t isolated advances. They’re interconnected. AI accelerates target discovery. Gene editing and degraders expand what’s therapeutically accessible. Diagnostics and spatial biology identify which patients will respond. CAR-T and other cell therapies deliver potentially curative treatments. Bioprocessing makes it all manufacturable at scale. Partnerships provide the specialized capabilities no single company can build alone. Data infrastructure enables the AI that started this cycle.
2025 proved that we can create breakthrough science. 2026 will prove that we can deliver it to patients. The companies that master the bridge from proof to production will define which breakthroughs actually reach patients and which remain confined to journals and conference presentations. The race isn’t just to innovate. It’s to scale innovation.
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