Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity.

Therapeutic proteins of wide-ranging function hold great promise for treating disease, but immune surveillance of these macromolecules can drive an antidrug immune response that compromises efficacy and even undermines safety. To eliminate widespread T-cell epitopes in any biotherapeutic and thereby mitigate this key source of detrimental immune recognition, we developed a Pareto optimal deimmunization library design algorithm that optimizes protein libraries to account for the simultaneous effects of combinations of mutations on both molecular function and epitope content. Active variants identified by high-throughput screening are thus inherently likely to be deimmunized. Functional screening of an optimized 10-site library (1,536 variants) of P99 ß-lactamase (P99ßL), a component of ADEPT cancer therapies, revealed that the population possessed high overall fitness, and comprehensive analysis of peptide-MHC II immunoreactivity showed the population possessed lower average immunogenic potential than the wild-type enzyme. Although similar functional screening of an optimized 30-site library (2.15 × 109 variants) revealed reduced population-wide fitness, numerous individual variants were found to have activity and stability better than the wild type despite bearing 13 or more deimmunizing mutations per enzyme. The immunogenic potential of one highly active and stable 14-mutation variant was assessed further using ex vivo cellular immunoassays, and the variant was found to silence T-cell activation in seven of the eight blood donors who responded strongly to wild-type P99ßL. In summary, our multiobjective library-design process readily identified large and mutually compatible sets of epitope-deleting mutations and produced highly active but aggressively deimmunized constructs in only one round of library screening.

Crystallization Inhibition Properties of Cellulose Esters and Ethers for a Group of Chemically Diverse Drugs: Experimental and Computational Insight.

Amorphous solid dispersions are widely used to enhance the oral bioavailability of poorly water-soluble drugs. Polymeric additives are commonly used to delay crystallization of the drug from the supersaturated solutions formed upon ASD dissolution by influencing the nucleation and growth of crystals. However, there is limited evidence regarding the mechanisms by which polymers stabilize supersaturated drug solutions. The current study used experiments and computational modeling to explore polymer-drug interactions in aqueous solutions. Nucleation induction times for supersaturated solutions of nine drugs in the presence of five newly synthesized cellulose-based polymers were evaluated. The polymers had carboxylic acids substituents with additional variations in the side-chain structure: (1) one with a single side chain and a carboxylic acid termination, (2) three with a branched side chain terminated with a carboxylic and an alcohol group (varying the cellulose linkage and the length of the hydrocarbon side chain), and (3) one with a branched side chain with two carboxylic acid end groups. The polymers with a short side chain and one carboxylic acid were effective, whereas the polymers with the two carboxylic acids or a long hydrocarbon chain were less effective. Atomic force microscopy experiments, evaluating polymer adsorption onto amorphous drug films, indicated that the effective polymers were uniformly spread across the surface. These results were supported by molecular dynamics simulations of a polymer chain in the presence of a drug aggregate in an aqueous environment, whereby the effective materials had a higher probability of establishing close contacts and more negative estimated free energies of interaction. The insights provided by this study provide approaches to design highly effective polymers to improve oral drug delivery.

Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate.

The immunogenicity of biotherapeutics can bottleneck development pipelines and poses a barrier to widespread clinical application. As a result, there is a growing need for improved deimmunization technologies. We have recently described algorithms that simultaneously optimize proteins for both reduced T cell epitope content and high-level function. In silico analysis of this dual objective design space reveals that there is no single global optimum with respect to protein deimmunization. Instead, mutagenic epitope deletion yields a spectrum of designs that exhibit tradeoffs between immunogenic potential and molecular function. The leading edge of this design space is the Pareto frontier, i.e. the undominated variants for which no other single design exhibits better performance in both criteria. Here, the Pareto frontier of a therapeutic enzyme has been designed, constructed, and evaluated experimentally. Various measures of protein performance were found to map a functional sequence space that correlated well with computational predictions. These results represent the first systematic and rigorous assessment of the functional penalty that must be paid for pursuing progressively more deimmunized biotherapeutic candidates. Given this capacity to rapidly assess and design for tradeoffs between protein immunogenicity and functionality, these algorithms may prove useful in augmenting, accelerating, and de-risking experimental deimmunization efforts.

Computationally driven deletion of broadly distributed T cell epitopes in a biotherapeutic candidate.

Biotherapeutics are subject to immune surveillance within the body, and anti-biotherapeutic immune responses can compromise drug efficacy and patient safety. Initial development of targeted antidrug immune memory is coordinated by T cell recognition of immunogenic subsequences, termed "T cell epitopes." Biotherapeutics may therefore be deimmunized by mutating key residues within cognate epitopes, but there exist complex trade-offs between immunogenicity, mutational load, and protein structure-function. Here, a protein deimmunization algorithm has been applied to P99 beta-lactamase, a component of antibody-directed enzyme prodrug therapies. The algorithm, integer programming for immunogenic proteins, seamlessly integrates computational prediction of T cell epitopes with both 1- and 2-body sequence potentials that assess protein tolerance to epitope-deleting mutations. Compared to previously deimmunized P99 variants, which bore only one or two mutations, the enzymes designed here contain 4-5 widely distributed substitutions. As a result, they exhibit broad reductions in major histocompatibility complex recognition. Despite their high mutational loads and markedly reduced immunoreactivity, all eight engineered variants possessed wild-type or better catalytic activity. Thus, the protein design algorithm is able to disrupt broadly distributed epitopes while maintaining protein function. As a result, this computational tool may prove useful in expanding the repertoire of next-generation biotherapeutics.

Measurement of ulnar variance from the lateral radiograph: a comparison of techniques.

PURPOSE: To determine the reliability of measuring ulnar variance on lateral wrist radiographs and to compare this technique with previously described methods. METHODS: Ulnar variance was measured in 100 normal wrist radiographs using the methods of perpendiculars, central reference point, and the lateral radiograph by 3 surgeons on 2 occasions. Intraobserver repeatability and agreement between raters and methods were assessed and compared. RESULTS: Intra- and interobserver reliability and agreement were both excellent using all 3 methods within a ± 1.0-mm cutoff. However, there was substantial pairwise disagreement in measures of ulnar variance between all 3 methods. CONCLUSIONS: This study demonstrates that, for measurement of ulnar variance, the methods of perpendiculars, central reference point, and lateral radiographic measurement each have clinically acceptable intraobserver repeatability and interobserver agreement. Despite their independent reliability, each method of radiographic determination of ulnar variance had considerable disagreement with the other methods, indicative of inherent inaccuracies in the techniques. The lateral radiograph uniquely allows for visualization of the amount of ulnar head protruding proximal or distal to the concave lunate facet and allows for a rapid estimation of pronosupination, which is known to affect ulnar variance. CLINICAL RELEVANCE: Determination of ulnar variance can be an important component of surgical decision making in various pathological conditions of the hand and wrist. Traditionally, it has been measured through methods using the posteroanterior wrist radiograph, but there are potential shortcomings with these methods, and use of the lateral radiograph may provide a more clinically relevant picture of ulnar variance. This study shows that measurement from the lateral radiograph provides similar reliability to previously accepted techniques.

Optimization algorithms for functional deimmunization of therapeutic proteins.

BACKGROUND: To develop protein therapeutics from exogenous sources, it is necessary to mitigate the risks of eliciting an anti-biotherapeutic immune response. A key aspect of the response is the recognition and surface display by antigen-presenting cells of epitopes, short peptide fragments derived from the foreign protein. Thus, developing minimal-epitope variants represents a powerful approach to deimmunizing protein therapeutics. Critically, mutations selected to reduce immunogenicity must not interfere with the protein's therapeutic activity. RESULTS: This paper develops methods to improve the likelihood of simultaneously reducing the anti-biotherapeutic immune response while maintaining therapeutic activity. A dynamic programming approach identifies optimal and near-optimal sets of conservative point mutations to minimize the occurrence of predicted T-cell epitopes in a target protein. In contrast with existing methods, those described here integrate analysis of immunogenicity and stability/activity, are broadly applicable to any protein class, guarantee global optimality, and provide sufficient flexibility for users to limit the total number of mutations and target MHC alleles of interest. The input is simply the primary amino acid sequence of the therapeutic candidate, although crystal structures and protein family sequence alignments may also be input when available. The output is a scored list of sets of point mutations predicted to reduce the protein's immunogenicity while maintaining structure and function. We demonstrate the effectiveness of our approach in a number of case study applications, showing that, in general, our best variants are predicted to be better than those produced by previous deimmunization efforts in terms of either immunogenicity or stability, or both factors. CONCLUSIONS: By developing global optimization algorithms leveraging well-established immunogenicity and stability prediction techniques, we provide the protein engineer with a mechanism for exploring the favorable sequence space near a targeted protein therapeutic. Our mechanism not only helps identify designs more likely to be effective, but also provides insights into the interrelated implications of design choices.

Amorphous solid dispersions containing residual crystallinity: Influence of seed properties and polymer adsorption on dissolution performance.

The solubility advantage of amorphous solid dispersions (ASDs) is contingent upon supersaturation being generated and maintained. If crystals are present within an ASD, these crystals directly result in lost solubility advantage, and may also seed crystal growth leading to desupersaturation. The goal of this study was to evaluate the impact of residual crystals on ASD supersaturation profiles. Indomethacin-copovidone (PVPVA) ASDs with different levels of residual crystallinity were manufactured by hot melt extrusion (HME). PVPVA at 5 and 50 µg/mL was found to be a highly effective nucleation and crystal growth inhibitor of indomethacin at high supersaturation. Evidence of polymer adsorption onto indomethacin crystals was observed by atomic force microscopy and scanning electron microscopy. HME ASDs containing 0-25% residual crystallinity demonstrated lost solubility advantage, along with minimal desupersaturation during non-sink dissolution testing. While bulk seeds did not properly represent the impact of residual crystals, extensive polymer adsorption onto residual seed crystals resulted in poisoned crystal growth, limiting the potential dissolution performance consequences. Several risk factors related to the presence of residual crystallinity were identified: polymeric crystal growth inhibition effectiveness, seed properties, and supersaturation conditions.

Biomechanical Comparison of 3 Syndesmosis Repair Techniques With Suture Button Implants.

BACKGROUND: Suture button fixation of syndesmotic injury is growing in popularity, as it has been shown to provide adequate stability in a more cost-effective manner than screw fixation while allowing more physiologic distal tibiofibular joint motion. However, the optimal repair technique and implant orientation have yet to be determined. PURPOSE/HYPOTHESIS: The purpose of this study was to biomechanically compare 3 suture button construct configurations/orientations for syndesmosis fixation: single, parallel, and divergent. The authors hypothesized that all 3 methods would provide adequate stabilization but that the divergent technique would be the most stable. STUDY DESIGN: Controlled laboratory study. METHODS: The fixation strengths of 3 stabilization techniques with suture button devices were compared with 10 cadaveric legs each (N = 30). Ankle motion under cyclic loading was measured in multiple planes: first in the intact state, then following simulated syndesmosis injury, and then following fixation with 1 of 3 randomly assigned constructs-1 suture button, 2 suture buttons in parallel, and 2 divergent suture buttons. Finally, axial loading with external rotation was applied to failure. RESULTS: All syndesmotic fixation methods provided stability to the torn state. There was no statistically significant difference among the 3 fixation techniques in biomechanical stability. Failure most commonly occurred through fibular fracture at supraphysiologic loads. CONCLUSION: Suture button implant fixation for syndesmotic injury appears to provide stability to the torn syndesmosis, and the configuration of the fixation does not appear to affect the strength or security of the stabilization. CLINICAL RELEVANCE: This study provides further insight into the biomechanics and optimal configuration of suture button fixation of the torn syndesmosis. Based on these results, the addition of a second suture button may not significantly contribute to immediate postoperative stability.

Structure-guided deimmunization of therapeutic proteins.

Therapeutic proteins continue to yield revolutionary new treatments for a growing spectrum of human disease, but the development of these powerful drugs requires solving a unique set of challenges. For instance, it is increasingly apparent that mitigating potential anti-therapeutic immune responses, driven by molecular recognition of a therapeutic protein's peptide fragments, may be best accomplished early in the drug development process. One may eliminate immunogenic peptide fragments by mutating the cognate amino acid sequences, but deimmunizing mutations are constrained by the need for a folded, stable, and functional protein structure. These two concerns may be competing, as the mutations that are best at reducing immunogenicity often involve amino acids that are substantially different physicochemically. We develop a novel approach, called EpiSweep, that simultaneously optimizes both concerns. Our algorithm identifies sets of mutations making such Pareto optimal trade-offs between structure and immunogenicity, embodied by a molecular mechanics energy function and a T-cell epitope predictor, respectively. EpiSweep integrates structure-based protein design, sequence-based protein deimmunization, and algorithms for finding the Pareto frontier of a design space. While structure-based protein design is NP-hard, we employ integer programming techniques that are efficient in practice. Furthermore, EpiSweep only invokes the optimizer once per identified Pareto optimal design. We show that EpiSweep designs of regions of the therapeutics erythropoietin and staphylokinase are predicted to outperform previous experimental efforts. We also demonstrate EpiSweep's capacity for deimmunization of the entire proteins, case analyses involving dozens of predicted epitopes, and tens of thousands of unique side-chain interactions. Ultimately, Epi-Sweep is a powerful protein design tool that guides the protein engineer toward the most promising immunotolerant biotherapeutic candidates.

Design and analysis of immune-evading enzymes for ADEPT therapy.

The unparalleled specificity and activity of therapeutic proteins has reshaped many aspects of modern clinical practice, and aggressive development of new protein drugs promises a continued revolution in disease therapy. As a result of their biological origins, however, therapeutic proteins present unique design challenges for the biomolecular engineer. For example, protein drugs are subject to immune surveillance within the patient's body; this anti-drug immune response can compromise therapeutic efficacy and even threaten patient safety. Thus, there is a growing demand for broadly applicable protein deimmunization strategies. We have recently developed optimization algorithms that integrate computational prediction of T-cell epitopes and bioinformatics-based assessment of the structural and functional consequences of epitope-deleting mutations. Here, we describe the first experimental validation of our deimmunization algorithms using Enterobacter cloacae P99 ß-lactamase, a component of antibody-directed enzyme prodrug cancer therapies. Compared with wild-type or a previously deimmunized variant, our computationally optimized sequences exhibited significantly less in vitro binding to human type II major histocompatibility complex immune molecules. At the same time, our globally optimal design exhibited wild-type catalytic proficiency. We conclude that our deimmunization algorithms guide the protein engineer towards promising immunoevasive candidates and thereby have the potential to streamline biotherapeutic development.

Optimization of therapeutic proteins to delete T-cell epitopes while maintaining beneficial residue interactions.

Exogenous enzymes, signaling peptides, and other classes of nonhuman proteins represent a potentially massive but largely untapped pool of biotherapeutic agents. Adapting a foreign protein for therapeutic use poses numerous design challenges. We focus here on one significant problem: modifying the protein to mitigate the immune response mounted against "non-self" proteins, while not adversely affecting the protein's stability or therapeutic activity. In order to propose such variants suitable for experimental evaluation, this paper develops a computational method to select sets of mutations predicted to delete immunogenic T-cell epitopes, as evaluated by a 9-mer potential, while simultaneously maintaining important residues and residue interactions, as evaluated by one- and two-body potentials. While this design problem is NP-hard, we develop an integer programming approach that works very well in practice. We demonstrate the effectiveness of our approach by developing plans for biotherapeutic proteins that, in previous studies, have been partially deimmunized via extensive experimental characterization and modification of limited segments. In contrast, our global optimization technique considers an entire protein and accounts for all residues, residue interactions, and epitopes in proposing candidates worth subjecting to experimental evaluation.

Optimization of combinatorial mutagenesis.

Protein engineering by combinatorial site-directed mutagenesis evaluates a portion of the sequence space near a target protein, seeking variants with improved properties (e.g., stability, activity, immunogenicity). In order to improve the hit-rate of beneficial variants in such mutagenesis libraries, we develop methods to select optimal positions and corresponding sets of the mutations that will be used, in all combinations, in constructing a library for experimental evaluation. Our approach, OCoM (Optimization of Combinatorial Mutagenesis), encompasses both degenerate oligonucleotides and specified point mutations, and can be directed accordingly by requirements of experimental cost and library size. It evaluates the quality of the resulting library by one- and two-body sequence potentials, averaged over the variants. To ensure that it is not simply recapitulating extant sequences, it balances the quality of a library with an explicit evaluation of the novelty of its members. We show that, despite dealing with a combinatorial set of variants, in our approach the resulting library optimization problem is actually isomorphic to single-variant optimization. By the same token, this means that the two-body sequence potential results in an NP-hard optimization problem. We present an efficient dynamic programming algorithm for the one-body case and a practically-efficient integer programming approach for the general two-body case. We demonstrate the effectiveness of our approach in designing libraries for three different case study proteins targeted by previous combinatorial libraries--a green fluorescent protein, a cytochrome P450, and a beta lactamase. We found that OCoM worked quite efficiently in practice, requiring only 1 hour even for the massive design problem of selecting 18 mutations to generate 107 variants of a 443-residue P450. We demonstrate the general ability of OCoM in enabling the protein engineer to explore and evaluate trade-offs between quality and novelty as well as library construction technique, and identify optimal libraries for experimental evaluation.