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550 <main id="main-content" class="body"> 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 <div class="blog-hero-1 section minh-full justify-end"> 569 570 <div class="blog-hero-1__center container"> 571 <div class="row"> 572 <div class="col s12"> 573 <div class="blog-hero-1__overlay" style="opacity: 0.3"></div> 574 <div class="blog-hero-1__contents"> 575 <div class="blog-hero-1__tags"> 576 <div class="blog-hero-1__tags-visible"> 577 578 <a href="https://officinae.bio/officinae-bio-stories/tag/mrna" class="blog-hero-1__tag badge badge--medium badge--white badge--lowercase">mRNA</a> 579 580 581 <a href="https://officinae.bio/officinae-bio-stories/tag/expression-control" class="blog-hero-1__tag badge badge--medium badge--accent badge--lowercase">expression control</a> 582 583 </div> 584 585 </div> 586 <div class="blog-hero-1__title txt--dark"> 587 <h1 class="display"><span id="hs_cos_wrapper_name" class="hs_cos_wrapper hs_cos_wrapper_meta_field hs_cos_wrapper_type_text" style="" data-hs-cos-general-type="meta_field" data-hs-cos-type="text">Nuanced Engineering: Rethinking Codon Optimization in mRNA Medicine</span></h1> 588 </div> 589 <div class="blog-hero-1__infobar blog-infobar blog-infobar--dark"> 590 591 <div class="blog-infobar__author"> 592 593 <div class="blog-hero-1__author-name">by <a href="https://officinae.bio/officinae-bio-stories/author/davide-de-lucrezia">Davide De Lucrezia</a></div> 594 </div> 595 596 <div class="blog-infobar__time"> 597 <svg width="15" height="15" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 512 512"><path fill="currentColor" d="M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8zm0 448c-110.5 0-200-89.5-200-200S145.5 56 256 56s200 89.5 200 200-89.5 200-200 200zm61.8-104.4l-84.9-61.7c-3.1-2.3-4.9-5.9-4.9-9.7V116c0-6.6 5.4-12 12-12h32c6.6 0 12 5.4 12 12v141.7l66.8 48.6c5.4 3.9 6.5 11.4 2.6 16.8L334.6 349c-3.9 5.3-11.4 6.5-16.8 2.6z"></path></svg> 598 599 600 4 601 min read 602 </div> 603 <div class="blog-infobar__date"> 604 <svg width="13" height="15" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 448 512"><path fill="currentColor" d="M400 64h-48V12c0-6.627-5.373-12-12-12h-40c-6.627 0-12 5.373-12 12v52H160V12c0-6.627-5.373-12-12-12h-40c-6.627 0-12 5.373-12 12v52H48C21.49 64 0 85.49 0 112v352c0 26.51 21.49 48 48 48h352c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm-6 400H54a6 6 0 0 1-6-6V160h352v298a6 6 0 0 1-6 6zm-52.849-200.65L198.842 404.519c-4.705 4.667-12.303 4.637-16.971-.068l-75.091-75.699c-4.667-4.705-4.637-12.303.068-16.971l22.719-22.536c4.705-4.667 12.303-4.637 16.97.069l44.104 44.461 111.072-110.181c4.705-4.667 12.303-4.637 16.971.068l22.536 22.718c4.667 4.705 4.636 12.303-.069 16.97z"></path></svg> Jun 15, 2025, 9:23:37â¯AM 605 </div> 606 </div> 607 </div> 608 </div> 609 </div> 610 </div> 611 </div> 612 613 <div class="blog-body-1 section"> 614 <div class="blog-body__center container"> 615 <div class="row"> 616 <div class="blog-body-1__main col s7 md-s10"> 617 <div class="blog-body-1__content"> 618 <span id="hs_cos_wrapper_post_body" class="hs_cos_wrapper hs_cos_wrapper_meta_field hs_cos_wrapper_type_rich_text" style="" data-hs-cos-general-type="meta_field" data-hs-cos-type="rich_text"><div id="hs_cos_wrapper_widget_167d2a98-df0b-4618-b85e-85237d830460" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 619 620 621<style>.module--167d2a98-df0b-4618-b85e-85237d830460{margin-bottom:48px;}.module--167d2a98-df0b-4618-b85e-85237d830460 .module__inner{width:550px;}</style> 622 <div class="module module--167d2a98-df0b-4618-b85e-85237d830460 module--section-intro module--block-center module--text-center"> 623 <div class="module__inner"> 624 625 <div class="section-intro section-intro--light txt--light"> 626 627 <div class="section-intro__overline">In a nutshell</div> 628 629 630 <div class="section-intro__title"> 631 <h2>Optimizing Wisely</h2> 632 </div> 633 634 <div class="section-intro__desc"> 635 <div class="section-intro__desc-inner clear"> 636 <p>Chasing maximum protein output might look good on a graph, but inside the cell, itâs a different story. Overloaded translation machinery, depleted tRNAs, and tangled proteins can reduce therapeutic efficacy. A balanced approach, using evolutionary algorithms to generate diverse sequences, ensures optimal eff
636iciency, stability, and therapeutic performanceâavoiding the pitfalls of over-optimization</p> 637 </div> 638 </div> 639 640 <div class="section-intro__divider section-intro__divider--auto"><div class="section-intro__divider-line"></div></div> 641 642 </div> 643 644 </div> 645 </div> 646</div> 647<div id="hs_cos_wrapper_widget_22f00204-b74b-4266-b200-c39c1fb2c84e" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 648 649 650 651<div class="module module--22f00204-b74b-4266-b200-c39c1fb2c84e module--rtext"> 652 <div class="module__inner"> 653 654 <div id="rtext__22f00204-b74b-4266-b200-c39c1fb2c84e" class="rtext rtext--box-none txt--light"> 655 <div class="rtext__content clear normal-text"> 656 <!--more--><h2 style="text-align: left;"><strong data-renderer-mark="true">When more isn't better<br></strong></h2> 657<p data-start="450" data-end="719">mRNA is emerging as a highly modular therapeutic platform, offering precise control over therapeutic outcomes. <span style="color: #009c97;"><strong>Its intrinsic programmability makes it ideal for precision medicine</strong></span>, enabling fine-tuned adjustments in sequence design to achieve specific therapeutic objectives, including protein replacement therapies, cancer immunotherapies such as onco-vaccines, gene editing tools, and ex vivo cell engineering.<br><br><span style="color: #009c97;"><strong>A critical aspect of sequence design involves maximizing protein expression in vivo</strong></span>âproducing greater amounts of protein more rapidly. This conventional approach optimizes codon usage within the coding region to enhance translation efficiency and overall protein yield.<br><br>While protein quantity is undoubtedly important, recent evidence indicates that <span style="color: #009c97;"><strong>pushing this approach to extremes may lead to unintended drawbacks</strong></span>. Excessive translation rates can <span style="color: #009c97;"><strong>overwhelm cellular resources</strong></span>, causing metabolic stress. Additionally, rapid protein synthesis can result in <span style="color: #009c97;"><strong>improper protein folding</strong></span>, triggering cellular stress pathways, potentially leading to protein aggregation or degradation and <span style="color: #009c97;"><strong>ultimately reducing therapeutic efficacy</strong></span>.</p> 658 </div> 659 660 </div> 661 662 </div> 663 </div> 664 665 666</div> 667<div id="hs_cos_wrapper_widget_a39bf5ac-56ce-4657-aa10-aa4c5f62c627" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><span class="hs-horizontal-spacer"></span></div> 668<div id="hs_cos_wrapper_widget_33933b52-2020-49cc-a905-4f2fba2cd165" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 669 670 671 672<div class="module module--33933b52-2020-49cc-a905-4f2fba2cd165 module--rtext"> 673 <div class="module__inner"> 674 675 <div id="rtext__33933b52-2020-49cc-a905-4f2fba2cd165" class="rtext rtext--box-none txt--light"> 676 <div class="rtext__content clear normal-text"> 677 <h2><strong data-renderer-mark="true">Beyond Speed: Rethinking Codon Optimization for therapeutic efficacy.</strong></h2> 678<p data-renderer-start-pos="1352">Current algorithms for codon optimization in mRNA therapeutics typically prioritize maximizing protein expression by aligning codon selection with the most abundant host cellular tRNAs. Common methodologies include metrics such as the codon adaptation index (CAI) and relative synonymous codon usage (RSCU), both of which quantify codon optimality based on frequency within highly expressed genes.<br><br>Early success with codon optimization was notably demonstrated using Green Fluorescent Protein (GFP), where recoding the jellyfish-derived gene to match human-preferred codons significantly boosted expression in mammalian cells. This breakthrough validated the concept that aligning codon choice with cellular tRNA availability could markedly enhance protein yield, establishing codon optimization as a foundational technique in molecular biology.<br><br>However, this simplified perspective neglects two critical biological considerations: (i) the proper functional conformation of the protein is more important than mere quantity, and (ii) the expression of the protein must not overstress the target cell.<br><br>For example, excessively maximizing CAI without considering secondary mRNA structure can inadvertently generate stable structural elements such as hairpins and loops. These structures impede ribosomal progression, affecting translation fidelity and efficie
678ncy.<br><br>Moreover, overly efficient translation can excessively increase ribosome density, potentially triggering translation-dependent mRNA decay pathways, which prematurely reduces mRNA stability and overall protein output.</p> 679<p data-renderer-start-pos="1352">Additionally, excessive optimization can cause metabolic burden and lead to protein misfolding due to accelerated elongation rates. Such stressors activate cellular stress responses, further diminishing therapeutic efficacy.<br><br>Together, these factors highlight the need for a balanced, context-sensitive optimization approach that integrates translation efficiency, ribosome density, mRNA structural integrity, and metabolic load, ensuring robust and sustainable therapeutic performance.</p> 680 </div> 681 682 </div> 683 684 </div> 685 </div> 686 687 688</div> 689<div id="hs_cos_wrapper_widget_222cddbb-0cb1-4d58-8d44-88dc5981af41" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><span class="hs-horizontal-spacer"></span></div> 690<div id="hs_cos_wrapper_widget_a420b518-f862-49d4-8d1e-a1d3c78ee64c" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 691 692 693 694 695 696 697 698<div class="module module--a420b518-f862-49d4-8d1e-a1d3c78ee64c module--image"> 699 <div class="module__inner"> 700 701 702 703 704 705 706 <img src="https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=1800&height=200&name=stylized_mrna_rotating.gif" alt="stylized_mrna_rotating" srcset="https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=360&name=stylized_mrna_rotating.gif 360w, https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=480&name=stylized_mrna_rotating.gif 480w, https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=720&name=stylized_mrna_rotating.gif 720w, https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=768&name=stylized_mrna_rotating.gif 768w, https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=1350&name=stylized_mrna_rotating.gif 1350w, https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=1440&name=stylized_mrna_rotating.gif 1440w, https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=1920&name=stylized_mrna_rotating.gif 1920w, https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=2700&name=stylized_mrna_rotating.gif 2700w, https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=2880&name=stylized_mrna_rotating.gif 2880w, https://officinae.bio/hs-fs/hubfs/stylized_mrna_rotating.gif?width=3600&name=stylized_mrna_rotating.gif 3600w" width="1800" height="200" loading="lazy" class="img--radius "> 707 708 709 710 </div> 711 </div> 712</div> 713<div id="hs_cos_wrapper_widget_708433e2-5305-4a9d-bfce-c95abfbcfadb" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><span class="hs-horizontal-spacer"></span></div> 714<div id="hs_cos_wrapper_widget_bd89ec2e-f6df-4425-a539-7d6718fb2d46" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 715 716 717 718<div class="module module--bd89ec2e-f6df-4425-a539-7d6718fb2d46 module--rtext"> 719 <div class="module__inner"> 720 721 <div id="rtext__bd89ec2e-f6df-4425-a539-7d6718fb2d46" class="rtext rtext--box-none txt--light"> 722 <div class="rtext__content clear normal-text"> 723 <h2><strong data-renderer-mark="true">Slow is Smooth</strong></h2> 724<p data-start="337" data-end="664">The beauty and complexity of codon optimization lie in the redundancy of the genetic code. With up to six synonymous codons encoding a single amino acid, <span style="color: #009c97;"><strong>any protein sequence can be translated from thousands of distinct nucleotide sequences</strong></span>. This degeneracy opens up a vast design space to fine-tune protein expression for optimal functionality, not just maximum quantity.<br><br>Rather than treating the coding sequence (CDS) as an isolated unit, <span style="color: #009c97;"><strong>
724we co-optimize it alongside the 5' and 3' untranslated regions (UTRs)</strong></span>. A well-optimized CDS must harmonize with its UTRs to ensure smooth ribosome entry and progression, avoiding stalls and traffic jams during translation.<br><br>Codon usage must be carefully balanced. Overusing high-frequency codons may accelerate elongation but can overload cellular metabolism and deplete specific tRNA pools. Underusing them, on the other hand, can unnecessarily slow translation. To navigate this trade-off, <span style="color: #009c97;"><strong>we leverage codon-level design rules that maintain optimal flow without overwhelming the system</strong></span>.<br><br>Secondary structure adds another layer of complexity. Codon choices influence local mRNA folding, and high GC content can create stable hairpins and loops that hinder ribosomal movement. <strong><span style="color: #009c97;">Our algorithms minimize disruptive structures by leveraging design strategies that preserve high translation efficiency.</span></strong><br><br>Ribosome density is equally critical. Excessive ribosome loading on an mRNA can lead to collisions and activate decay pathways. <span style="color: #009c97;"><strong>Our protein sequence optimization tool balances codon usage and mRNA structure to prevent these bottlenecks and support continuous, high-fidelity translation.</strong></span><br><br>The ability to generate multiple synonymous DNA sequences for any single amino acid sequence expands the design space enormously. But <span style="color: #009c97;"><strong>exploring this space effectively requires more than brute force, it demands strategy</strong></span>.<br><br>To do this, <span style="color: #009c97;"><strong>we use a genetic algorithm, a population-based optimization method inspired by evolution, to explore a vast landscape of synonymous sequences and identify those that best balance expression efficiency, structural stability, and manufacturability.</strong></span></p> 725<p data-start="337" data-end="664">It begins with a diverse pool of candidate sequences, all encoding the same protein. Each sequence is evaluated in silico for translation efficiency, structural properties, and manufacturability. The best performers are selected, recombined, and mutated to form the next generation. This iterative process continues until convergence on a set of optimized sequences that balance expression, folding, and translational smoothness without inducing cellular stress.<br><br><span style="color: #009c97;"><strong>At the end of this in silico evolution, we return one or multiple optimized CDS sequences paired with their respective UTRs, ready for synthesis and experimental testing</strong></span>.<br><br>This approach offers several key advantages. First, it <span style="color: #009c97;"><strong>enables rapid exploration of thousands of sequence variants entirely in silico</strong></span>, saving time and resources.</p> 726<p data-start="337" data-end="664">Second, by <strong><span style="color: #009c97;">returning multiple high-quality candidates</span></strong>, it increases the probability of success through parallel testingâmore shots on goal.</p> 727<p data-start="337" data-end="664">Finally, experimental feedback can be used to refine Officinae Bioâs predictive models, enabling the development of a dedicated, <span style="color: #009c97;"><strong>user-specific model that customizes the design space to meet each customerâs unique requirements</strong></span>.</p> 728 </div> 729 730 </div> 731 732 </div> 733 </div> 734 735 736</div> 737<div id="hs_cos_wrapper_widget_d79222f4-20f2-46e9-89f3-f6de864a52a7" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 738 739 740<style>.module--d79222f4-20f2-46e9-89f3-f6de864a52a7{margin-bottom:48px;}.module--d79222f4-20f2-46e9-89f3-f6de864a52a7 .module__inner{width:550px;}</style> 741 <div class="module module--d79222f4-20f2-46e9-89f3-f6de864a52a7 module--section-intro module--block-center module--text-center"> 742 <div class="module__inner"> 743 744 <div class="section-intro section-intro--light txt--light"> 745 746 747 <div class="section-intro__desc"> 748 <div class="section-intro__desc-inner clear"> 749 <h2>Slow is smooth.<br>And in codon optimization, smooth is smart.</h2> 750 </div> 751 </div> 752 753 <div class="section-intro__divider section-intro__divider--auto"><div class="section-intro__divider-line"></div></div> 754 755 </div> 756 757 </div> 758 </div> 759</div> 760<div id="hs_cos_wrapper_widget_557132dd-09cc-422e-bc09-09e6ac424ecc" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><span class="hs-horizontal-spacer"></span></div> 761<div id="hs_cos_wrapper_widget_703f2503-69ef-438e-b417-8c8cad973050" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 762 763 764 765<div class="module module--703f2503-69ef-438e-b417-8c8cad973050 module--rtext"> 766 <div class="module__inner"> 767 768 <div id="rtext__703f2503-69ef-438e-b417-8c8cad973050" class="rtext rtext--box-none txt--light"> 769 <div class="rtext__content clear normal-text"> 770 <h2><strong data-renderer-mark="true">One size doesn't fit all<br></strong></h2> 771<p data-start="1911" data-end="2101">Most codon optimization algorithms, including ours, are trained on an abstraction: <span style="color: #009c97;"><strong>the average mammalian cell expressing an average protein. The problem? That cell doesnât exist. And neither does the generic protein.</strong></span><br><br>In reality, codon usage bias, tRNA availability, translation kinetics, and metabolic thresholds vary significantly between cell types. A lung endothelial cell does not process mRNA the same way a T cell does. The folding landscape of a soluble cytokine is not equivalent to that of a transmembrane T-cell receptor. <span style="color: #009c97;"><strong>Biology is context-dependent and the algorithms that engineer it should be too.</strong></span><br><br>The future lies in cell-type specific protein-aware design models: <span style="color: #009c97;"><strong>algorithms that account for the distinct translational environments of specific cell types and the biophysical demands of different protein classes.</strong></span> Imagine optimization pipelines that anticipate the folding bottlenecks of membrane proteins or adapt to the translation burden limits of exhausted T cells. That is
771where we are headed.</p> 772 </div> 773 774 </div> 775 776 </div> 777 </div> 778 779 780</div> 781<div id="hs_cos_wrapper_widget_93753633-b9b9-4175-9a89-a43cfb5b9858" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 782 783 784<style>.module--93753633-b9b9-4175-9a89-a43cfb5b9858{margin-bottom:48px;}.module--93753633-b9b9-4175-9a89-a43cfb5b9858 .module__inner{width:550px;}</style> 785 <div class="module module--93753633-b9b9-4175-9a89-a43cfb5b9858 module--section-intro module--block-center module--text-center"> 786 <div class="module__inner"> 787 788 <div class="section-intro section-intro--light txt--light"> 789 790 791 <div class="section-intro__desc"> 792 <div class="section-intro__desc-inner clear"> 793 <h2>Precision medicine deserves precision design. <br>One size never fits all.</h2> 794 </div> 795 </div> 796 797 <div class="section-intro__divider section-intro__divider--auto"><div class="section-intro__divider-line"></div></div> 798 799 </div> 800 801 </div> 802 </div> 803</div> 804<div id="hs_cos_wrapper_widget_36f93f8d-a42c-47c9-8b41-d99abda915e0" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space widget-type-space" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><span class="hs-horizontal-spacer"></span></div> 805<div id="hs_cos_wrapper_widget_333c7dbe-7b38-4fe6-8439-e5b68cae797f" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 806 807 808 809<div class="module module--333c7dbe-7b38-4fe6-8439-e5b68cae797f module--rtext"> 810 <div class="module__inner"> 811 812 <div id="rtext__333c7dbe-7b38-4fe6-8439-e5b68cae797f" class="rtext rtext--box-none txt--light"> 813 <div class="rtext__content clear normal-text"> 814 815 </div> 816 817 </div> 818 819 </div> 820 </div> 821 822 823</div> 824<div id="hs_cos_wrapper_widget_a4bd2715-fa4a-4f53-8381-27b69df6cce6" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 825 826<div class="hs-social-follow"> 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 <a rel="nofollow noopener" class="hs-social-follow__link hs-social-follow__link--linkedin" target="_blank" aria-label="Follow us on LinkedIn" href="https://www.linkedin.com/company/81432159/admin/feed/posts/"> 846 <span id="hs_cos_wrapper_widget_a4bd2715-fa4a-4f53-8381-27b69df6cce6_" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_icon hs-social-follow__icon" style="" data-hs-cos-general-type="widget" data-hs-cos-type="icon"><svg version="1.0" xmlns="http://www.w3.org/2000/svg" viewbox="0 0 448 512" aria-hidden="true"><g id="LinkedIn2_layer"><path d="M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z" /></g></svg></span> 847 </a></div></div></span> 848 </div> 849 </div> 850 <div class="blog-body-1__spacer col s2 lg-hidden sm-hidden" style="order: 3;"></div> 851 <div class="blog-body-1__aside-2 col s3 md-s10"> 852 853 <div id="hs_cos_wrapper_aside_2" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 854 855 856 857<div class="module module--aside_2 module--rtext"> 858 <div class="module__inner"> 859 860 <div id="rtext__aside_2" class="rtext rtext--box-none txt--light img--radius"> 861 <div class="rtext__content clear normal-text"> 862 863 </div> 864 865 </div> 866 867 </div> 868 </div> 869 870 871</div> 872 873 </div> 874 875 <div class="blog-body-1__aside-1 col s2"> 876 <div class="blog-body-1__sharing"> 877 <h4 class="blog-body-1__sharing-title">Share this</h4> 878 <div class="clear"> 879 880 881 <div id="hs_cos_wrapper_sharing" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"> 882 883 884 885 886 887 888<div class="module module--sharing module--sharing"> 889 <div class="module__inner"> 890 891 <div class="sharing txt--light"> 892 893 <div class="sharing__icons inline-items" style="gap:.56rem"> 894 895 <a class="sharing__icon sharing__icon--twitter" href="https://twitter.com/share?url=https://
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https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=1920&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 1920w, https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=2278&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 2278w" loading="lazy"></div> 1007 1008 <div class="blog-card__body"> 1009 <div class="blog-card__text"> 1010 1011 <div class="blog-card__tags"> 1012 <a href="https://officinae.bio/officinae-bio-stories/tag/mrna"><span class="badge badge--medium badge--accent badge--lowercase">mRNA</span></a> 1013 </div> 1014 1015 <h4 class="blog-card__title">Beyond Peak Expression: Rethinking Control in mRNA Therapeutics</h4> 1016 </div> 1017 <div class="blog-card__info 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1018 <span class="blog-card__date">Jun 11, 2025, 5:11:18â¯PM</span> 1019 <span class="blog-card__readtime"> 1020 1021 1022 2 1023 min read</span> 1024 </div> 1025 </div> 1026 </div> 1027 </div> 1028 </div> 1029 1030 1031 1032 1033 <div class="col s4 md-s6 sm-s12 flex-row"> 1034 <div class="blog-card blog-card--white blog-card--shadow blog-card--with-img"> 1035 <a class="blog-card__link" href="https://officinae.bio/officinae-bio-stories/from-generic-to-smart-its-time-to-rethink-mrna-utrs"><span class="screen-reader-text">From Generic to Smart: Itâs Time to Rethink mRNA UTRs</span></a> 1036 <div class="blog-card__content"> 1037 1038 <div class="blog-card__img"><img src="https://officinae.bio/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png" alt="" srcset="https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=360&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 360w, https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=480&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 480w, https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=720&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 720w, https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=768&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 768w, https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=1350&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 1350w, https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=1440&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 1440w, https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=1920&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 1920w, https://officinae.bio/hs-fs/hubfs/aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png?width=2278&name=aofficinaebio_code_to_DNA_molecule_glass_painting_hyperrealistic_e537c5bb-c081-4860-a1b0-b896c46e7bb9.png 2278w" loading="lazy"></div> 1039 1040 <div class="blog-card__body"> 1041 <div class="blog-card__text"> 1042 1043 <div class="blog-card__tags"> 1044 <a href="https://officinae.bio/officinae-bio-stories/tag/mrna"><span class="badge badge--medium badge--accent badge--lowercase">mRNA</span></a> 1045 </div> 1046 1047 <h4 class="blog-card__title">From Generic to Smart: Itâs Time to Rethink mRNA UTRs</h4> 1048 </div> 1049 <div class="blog-card__info small-text"> 1050 <span class="blog-card__date">Jun 13, 2025, 9:30:08â¯AM</span> 1051 <span class="blog-card__readtime"> 1052 1053 1054 2 1055 min read</span> 1056 </div> 1057 </div> 1058 </div> 1059 </div> 1060 </div> 1061 1062 1063 1064 </div> 1065 </div> 1066 </div> 1067 1068 1069 <div class="blog-sub section hs-search-hidden"> 1070 <div class="blog-sub__center container"> 1071 <div class="row"> 1072 <div class="col s12"> 1073 <div class="blog-sub__icon"> 1074 <figure class="icon icon--large icon--disc icon--fill icon--white-accent"> 1075 <div class="icon__glyph"> 1076 <svg width="24" height="24" viewbox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"> 1077 <path fill-rule="evenodd" clip-rule="evenodd" d="M12 2C10.8954 2 10 2.89543 10 4V4.34141C7.66961 5.16508 6 7.38756 6 10L6 11.5L6 13.5996L4.38253 15.8237C3.94008 16.432 3.87642 17.2372 4.21777 17.9076C4.55913 18.5779 5.24775 19 6 19H9C9 20.6569
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