Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
The present application was filed on 05/11/2022. Acknowledgment is made of the present application as a proper National Stage (371) entry of PCT/EP2020/084366, filed on 12/03/2020, which claims benefit of the foreign Application No. United Kingdom 1917630.4, filed on 12/03/2019.
Claim status
Claims 6, 8, 17-18, 20-23, and 30 are withdrawn. Claims 2, 12-13 and 19 are canceled.
Claims 1, 3-5, 7, 9-11, 14-16, 24-29 and 31-34 are examined herein.
Update Objections/Rejections
The rejections of claim 19 under 35 USC 112(b) and 35 USC 101 are withdrawn in view of the cancelation of claim 19.
The rejections of claims 1, 3-5, 7, 9-11, 14-16, 24-29 and 31-34 under 35 USC 103 are updated in view of the amendment of the claim and Applicant’s arguments filed on 11/21/2025.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-5, 7, 10-11, 14-15, 24-29 and 31-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lofas (WO 2011/093782) in view of GE Healthcare (Biacore Sensor Surface Handbook, 2007, PTO-892 dated 08/27/2025), Karlsson (US20170242001), Wang et al. (Non-Size-Based Membrane Chromatographic Separation and Analysis of Monoclonal Antibody Aggregates, Anal. Chem. 2006, 78, 6863-6867, IDS 08/12/2025) and Reeve et al. (US20090192048, IDS 08/12/2025).
Regarding claims 1 and 24-26, Lofas teaches a method of determining aggregates in a fluid containing the macromolecule, comprising the steps of: contacting a sample of the fluid with a sensing surface of an interaction analysis sensor, wherein the sensing surface is capable of specific binding interaction with the macromolecule; determining at least one kinetic parameter for the interaction of the fluid sample with the sensing surface; comparing the determined kinetic parameter or parameters with that or those determined for at least one fluid sample having a known fraction or fractions of aggregates of the macromolecule; and determining therefrom the fraction of macromolecule in the sample that is in the form of aggregate or aggregates. See Abstract.
Lofas particularly discloses that the sensor surface having a binding partner (i.e., a ligand) to the macromolecule immobilized thereon (see page 2 par.3). Lofas teaches that the interaction analysis sensor is a mass-sensing biosensor based on surface plasmon resonance (SPR) evanescent wave sensing (see page 3 lines 15-16, see page 4 par.7 and page 5 par.1). Lofas further teaches that the sensor include the flow-through cell-based Biacore® systems (GE Healthcare Bio-Sciences AB, Uppsala, Sweden) (see page 5 par.2).
However, Lofas does not teach the ligand comprising a hydrophobic group. Lofas does not teach that the log P value of the hydrophobic group should be greater than 0.
GE Healthcare teaches Biacore® systems exploiting the phenomenon of surface plasmon resonance (SPR) to monitor the interaction between molecules in real time. See page 7 section 1.1. The system can determine a sample comprising aggregates (see page 32, par.1: the chip can be an advantage in experiments involving large molecular aggregates, virus particles and whole cells). The method of detecting an analyte in a sample comprises: step a/ wherein a hydrophobic ligand is immobilized on a surface of the sensor chip (see pages 10-11 section 1.3.2); step b/ and step c/ ii wherein parameters are kinetics and affinity of an interaction, investigated by analyzing the binding behavior in terms of molecular interaction models (see page 7 section 1.1); step d/ determining the concentration of the specific molecules present in the sample (see page 7 section 1.1).
GE Healthcare also offers a wide selection of different sensor surfaces to fill the varied application requirements for SPR technology e.g., chip CM5 which is used in the method of Lofas (see page 15 section 2.2).
Particularly, GE Healthcare discloses that the surface of sensor chip CM5 is covered with a matrix of carboxymethylated dextran wherein a variety of ligands can be attached such as small organic molecules to proteins, nucleic acids and carbohydrates (see page 14, section 2.1.2; see page 16 section 2.2.1). The chip CM5 can be modified to chip L1, which has a surface matrix of carboxymethyl dextran similar to that on Sensor Chip CM5, to which lipophilic residues have been covalently attached (see page 18 section Sensor Chip L1). Therefore, the sensor chip taught by GE Healthcare can have a ligand comprising a hydrophobic group on its surface, can be used as a surface plasmon resonance (SPR) sensors, and can be used with a Biacore® system (see pages 14-16 and 18).
Karlsson provides an example of a mass-sensing biosensor based on surface plasmon resonance evanescent wave sensing (see pars.60-61 and 70-75) from Biacore® system. Karlsson teaches an analytical system for studying molecular interactions (see Abstract). The analytical system is a mass-sensing biosensor based on surface plasmon resonance evanescent wave sensing (see pars.60 and 75). The sensor surface contains immobilized partners for the analyte, which are ligands comprising functional groups capable of interaction with a target compound, e.g., a hydrophobic group (see pars.11, 16 and 52).
Wang provides a hydrophobic interaction membrane chromatography-based technique for rapid, non-size-based separation and analysis of the aggregate content in monoclonal antibody sample. The technique is based on the fact that monoclonal antibody aggregates are more hydrophobic than the monomer form, so the antibody and its aggregated form could be eluted out as separate peaks in order of increasing hydrophobicity. See Abstract. In other words, the hydrophobic surface is capable of increased binding interaction with aggregated antibody compared to non-aggregated antibody (see page 6865 col.2 par.1: teaching that the antibody aggregates were more hydrophobic than the antibody itself, and an attempt was made to fractionate Campath-9 from its aggregates using HIMC).
Reeve teaches a method of producing a multimeric capture agent for binding a ligand (see Abstract), wherein the capture agent comprises at least first and second monomers units (see par.8). The monomer unit can be any suitable type of molecule, e.g., amino acids (see par.28). The amino acid may provide a hydrophobic moiety for ligand binding (see par.46). The hydrophobic amino acids are selected from the group consisting of leucine, isoleucine, valine, norvaline, methionine, tyrosine, tryptophan and phenylalanine. More preferably, the hydrophobic amino acid is phenylalanine (see par.84).
Since the hydrophobic group can consist of leucine, isoleucine, valine, norvaline, methionine, tyrosine, tryptophan and phenylalanine (see Reeve par.84), therefore it appears to have a log P value>0 as discloses in the instant specification (par.65-69: the term “hydrophobic group” as used herein is defined as a group of molecules which has a log P value>0, e.g., comprise the side chain of an amino acid selected from the group consisting of alanine, valine, leucine, isoleucine, methionine, phenylalanine, tryptophan, and tyrosine, or a hydrophobic derivative of said side chain, preferably the side chain of an amino acid selected from the group consisting of phenylalanine, tryptophan, and tyrosine).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the binding partner on the surface of the sensor of Lofas, by using the ligand comprising a hydrophobic functional group capable of interaction with a target compound as taught by GE Healthcare and Karlsson to determine aggregates comprising one or more macromolecules in a sample. One having an ordinary skill in the art would have been motivated to use the ligand having a hydrophobic functional group on the sensor surface because it is capable of increased binding interaction with aggregated molecule as taught by Wang, thereby being helpful for the method of detecting aggregates of Lofas. As such, it would be obvious to use the ligand comprising the hydrophobic amino acid residue for ligand binding as taught by Reeve, wherein the hydrophobic amino acids comprise valine, leucine, tyrosine, tryptophan, phenylalanine, isoleucine, methionine. Accordingly, the hydrophobic group has a log P value >0 as discuss above.
One having an ordinary skill in the art would have had a reasonable expectation of success in combining Lofas, GE Healthcare and Karlsson because Lofas is directed to the method of determining aggregates of a macromolecule using the sensor chip and the Biacore® system from GE Healthcare, wherein the sensor is a mass-sensing biosensor based on surface plasmon resonance (SPR) evanescent wave sensing (see Lofas page 3 lines 15-16, see page 4 par.7 page 5 par.1). GE Healthcare teaches that the Biacore sensor chip can have a ligand comprising a hydrophobic group on its surface (see GE Healthcare pages 14-16 and 18). Karlsson provides an example of a mass-sensing biosensor based on surface plasmon resonance evanescent wave sensing (see pars.60-61 and 70-75) from Biacore® system for studying molecular interactions, which the sensor surface contains immobilized hydrophobic ligands for binding the analyte (see Karlsson Abstract pars.11, 16, 52, 60-61 and 70-75). Therefore, the sensor chips taught by Lofas, GE Healthcare and Karlsson are analogous in terms of design and function. Moreover, Lofas is generic to a sensor chip that has a binding partner to the analyte immobilized thereon (see Lofas page 2 par.3) and GE Healthcare and Karlsson provide a specific sensor chip that has a binding partner having hydrophobic groups. The modified sensor of Lofas in view of GE Healthcare and Karlsson can also be used for determining aggregates because the hydrophobic sensor surface can interact with aggregated molecules better than non-aggregated molecules as taught by Wang.
Regarding claims 3-5 and 27-29, Lofas in view of GE Healthcare, Karlsson, Wang, and Reeve teaches the method of claim 1. Lofas does not teach the hydrophobic group comprising the side chain of an amino acid selected from the group consisting of alanine, valine, leucine, isoleucine, methionine, phenylalanine, tryptophan, and tyrosine, or a hydrophobic derivative of said side chain, preferably the side chain of an amino acid selected from the group consisting of phenylalanine, tryptophan, and tyrosine as in claims 3-5 and 27-29.
Lofas in view of GE Healthcare, Karlsson, and Wang teaches that the sensor chip having an immobilized ligand comprising hydrophobic or lipophilic groups on the surface can be used to determine aggregated molecule (see discussion in claim 1 above).
Reeve teaches a ligand comprising hydrophobic groups that can be immobilized on a surface to capture a target analyte (see Reeve at least in Abstract, pars.8, 28, 46 and 84). See discussion in claim 1 above. The ligand comprises a side chain of an amino acid selected from the group consisting of leucine, isoleucine, valine, methionine, tyrosine, tryptophan and phenylalanine (see Reeve pars.44-46, 84).
Since it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the binding partner on the surface of the sensor of Lofas, by using the ligand comprising a hydrophobic functional group capable of interaction with a target compound as taught by GE Healthcare and Karlsson to determine aggregated macromolecules in a sample because the reasons discussed above in claim 1. Therefore, it would have been obvious to use the capture agent comprising the hydrophobic amino acid residue for ligand binding taught by Reeve wherein the hydrophobic amino acids comprise valine, leucine, tyrosine, tryptophan, phenylalanine, isoleucine, methionine.
One having an ordinary skill in the art would have had a reasonable expectation of success in using the capture agent taught by Reeve because Lofas is generic to a sensor chip that has a binding partner to the analyte immobilized thereon (see Lofas page 2 par.3) and Reeve provides a specific binding partner comprising the hydrophobic amino acid residue, which can be used to detect aggregated molecules as taught by Wang.
Regarding claim 7, Lofas in view of GE Healthcare, Karlsson, Wang, and Reeve teaches the method of claim 1. Lofas teaches a step of comparing the determined kinetic parameter or parameters with that or those determined for at least one fluid sample having a known fraction or fractions of aggregates of the macromolecule, and determining therefrom the fraction of macromolecule in the sample that is in the form of aggregate or aggregates (see abstract).
Regarding claims 10 and 31, Lofas in view of GE Healthcare, Karlsson, Wang, and Reeve teaches the method of claims 1 and 24. Lofas teaches that the macromolecule may be determined by the method is typically a protein or polypeptide, particularly a therapeutic protein or polypeptide, such as an antibody, but may also be, for example, a nucleic acid (see page 3, par.3).
Regarding claims 11 and 32, Lofas in view of GE Healthcare, Karlsson, Wang, and Reeve teaches the method of claims 1 and 24. Lofas teaches the sensor include the flow-through cell-based Biacore® systems (GE Healthcare Bio-Sciences AB, Uppsala, Sweden) (see page 5 par.2). GE Healthcare teaches that the sensor chip comprises a ligand immobilized on the sensor surface which the ligand comprises common functional groups such as amino group (see page 16 section 2.2.1) or carboxyl group (see page 18 section 2.2.4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the method of Lofas for determining of aggregates comprising one or more macromolecules in a sample because the method of Lofas uses the biosensor of GE Healthcare having a ligand which comprises common functional groups such as amino group or carboxyl group so that the ligand can interact with the analyte of interest as taught by GE Healthcare (see GE Healthcare page 7, section 1.2).
One having an ordinary skill in the art would have had a reasonable expectation of success in combining Lofas and GE Healthcare because Lofas is directed to the method of determining aggregates of a macromolecule using the sensor chip from GE Healthcare.
Regarding claims 14-15, Lofas in view of GE Healthcare, Karlsson, Wang, and Reeve teaches the method of claim 1. Lofas teaches the method can describe the progress of the molecular interaction in real time with Biacore system including an association phase part and a dissociation phase part (see page 5 par.3-4). The method also comprises calculating the association rate, dissociation rate (see page 7 par.2).
Regarding claims 33-34, Lofas in view of GE Healthcare, Karlsson, Wang, and Reeve teaches the method of claim 24. Lofas teaches that the interaction analysis sensor is preferably a biosensor, especially a mass-sensing biosensor (see page 3 lines 15-16), or surface plasmon resonance (SPR) sensors (see page 5 par.1).
Claim(s) 9 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lofas in view of GE Healthcare, Karlsson, Wang, and Reeve, as applied in claim 1 above, and further in view of Pol et al. (Evaluation of calibration-free concentration analysis provided by Biacore™ systems, Analytical Biochemistry 510 (2016) 88-97, PTO-892 dated 08/27/2025).
Regarding claims 9 and 16, Lofas in view of GE Healthcare, Karlsson, Wang, and Reeve teaches the method of claim 1. Lofas teaches that the method using the sensor, e.g., Biacore of GE Healthcare can determine mass detection (see page 4 last paragraph). GE Healthcare provides that the sensor chip and system can determine the molecular weight of the aggregate(s) (see page 68 section 6.1.4/2). Lofas does not teach the step c (ii) comprising a diffusion coefficient determination for the aggregates.
However, Pol teaches Surface Plasmon Resonance biosensors measure the interaction between a molecule in solution and its interaction partner attached to a sensor surface (see Abstract), e.g. Biacore platform with Biacore T200 and Biacore X100 evaluation software (see page 88 col.2 par.2). The software can conduct Calibration Free Concentration Analysis (CFCA) including diffusion coefficient (see at least page 89 and Table 1; see page 91 section 3.3).
According to Pol, CFCA does not require a standard and makes it possible to determine the active concentration directly. This method requires that binding rates are at least partially limited by transport effects, that the diffusion coefficient of the molecule can be determined and that the Biacore response can be converted into mass. Whereas, traditionally, the active binding concentration measurements provide a relative values as a new sample is compared to a standard. However, proteins may be differently expressed or may have undergone post translational modifications that influence binding activity, and for that reason the relevance of the standard can be questioned and the absolute active concentration may never be known. See pages 95-96 section 5.
Moreover, Pol provides that by examining experimental conditions including immobilization levels and temperature for a range of analytes, and by using global analysis of several sample dilutions, conditions that gave the most robust results were identified (see Abstract). Pol notes that CFCA, e.g., diffusion coefficient value, may be temperature-dependent (see page 91 section 3.3; see page 94-95 section 4.2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Lofas, determining a diffusion coefficient for the aggregates at different temperatures because Pol teaches that the active concentration of an aggregate can be directly determined without the use of standard, and the Biacore platform can convert the diffusion coefficient into mass. Since the CFCA, e.g., diffusion coefficient value, may be temperature-dependent, experiment should be done in a varied temperature to get the accuracy, precision and robustness of CFCA (see section 4.3 page 94-95; page 96 col.2 par.4).
One having an ordinary skill in the art would have had a reasonable expectation of success in combining Lofas and Pol because Lofas uses Biacore system and chip to determine aggregates of a macromolecule monomer in a fluid and Pol further teaches the Biacore platform can do CFCA including the diffusion coefficient at a different temperatures to achieve the accuracy, precision and robustness of CFCA.
Response to Arguments
Applicant's arguments filed 11/21/2025 have been fully considered but they are not persuasive.
Applicant argues that Lofas does not disclose or suggest use of a hydrophobic ligand that generally binds aggregate analytes, where the degree of aggregation is manifested in the amount of binding. Rather, Lofas teaches that the ligand binds specifically to the target analyte molecules, such that the degree of aggregation is manifested in the kinetic profile with which the analyte binds. The method of Lofas therefore utilizes a different interaction principle based on specific molecular binding, rather than general hydrophobic interactions as claimed, and is not capable of generating a response quantification as Applicant's claims require.
Examiner agrees that Lofas does not teach the use of a hydrophobic ligand on the sensor surface, but it would have been obvious to modify the binding partner on the surface of the sensor of Lofas, by using the ligand comprising a hydrophobic functional group capable of interaction with a target compound as taught by GE Healthcare and Karlsson to determine aggregates because the ligand comprising a hydrophobic functional group is capable of increased binding interaction with aggregated molecule as taught by Wang, thereby being helpful for the method of detecting aggregates of Lofas.
While Lofas emphasizes that the degree of aggregation is manifested in the kinetic profile with which the analyte binds, the sensor system of Lofas can also do what the typical sensor can do, e.g., detecting mass surface concentration (see Lofas page 4 last paragraph). The sensor of Lofas can determine the presence and concentration of a particular molecule, or analyte in a sample (see Lofas page 5 par.4). Therefore, if the sensor of Lofas is modified with the ligand comprising a hydrophobic functional group, the degree of aggregation would be manifested in the amount of general hydrophobic interactions because the ligand comprising a hydrophobic functional group is capable of increased binding interaction with aggregated molecule and the sensor of Lofas can detect the change of mass on the sensor surface (Lofas page 4 last par.).
Moreover, the claim does not limit the manifestation of the degree of aggregation based on only the amount of binding. The claim recites “determining the presence, fraction, concentration, and/or amount of macromolecules in the form of aggregates in the first sample”. Lofas at least teaches to determine the presence and fraction of macromolecules in the form of aggregates in the sample (see Lofas pages 5-6: teaching the degree of aggregation is manifested in the kinetic profile with which the analyte binds, wherein the kinetic behavior may be due to several factors such as the larger mass/volume of the aggregates compared to monomer so that an aggregate will give a greater response, i.e. faster on-rate, at a mass sensing surface than a monomer. The greater the fraction of aggregate is in a monomer/ aggregate sample, the greater the initial slope will be, and the more the initial slope will differ from that determined for a sample containing only monomer). Therefore, Lofas encompasses the limitation of determining the degree of aggregation. The Applicant’s argument is not found persuasive.
Applicant argues that Lofas does not disclose or suggest a method involving binding of a hydrophobic ligand, Lofas does not disclose or suggest a method where the hydrophobic group of the ligand has a log P value > 0. Lofas does not utilize hydrophobic binding ligands, and as such, one of ordinary skill in the art would have no reason to combine the teachings of Lofas and Wang to arrive at Applicant's claimed methods, nor would such a modification of Lofas even be operable.
The argument is not found persuasive because it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the binding partner on the surface of the sensor of Lofas, by using the ligand comprising a hydrophobic functional group capable of interaction with an analyte a target compound as taught by GE Healthcare and Karlsson to determine aggregates comprising one or more macromolecules in a sample. One having an ordinary skill in the art would have been motivated to use the ligand having a hydrophobic functional group on the sensor surface because it is capable of increased binding interaction with aggregated molecules as taught by Wang. As such, it would be obvious to use the ligand comprising the hydrophobic amino acid residue for ligand binding as taught by Reeve, wherein the hydrophobic amino acids comprise valine, leucine, tyrosine, tryptophan, phenylalanine, isoleucine, methionine. Accordingly, the hydrophobic group has a log P value >0 as discussed above.
Examiner adds a new reference Karlsson to support that the sensor system comprising a ligand having a hydrophobic functional group on the sensor surface has been well known in the art of studying molecular interactions. The modified sensor of Lofas is still operable because the sensor chips taught by Lofas, GE Healthcare and Karlsson are analogous in terms of design and function. Moreover, Lofas is generic to a sensor chip that has a binding partner to the analyte immobilized thereon (see Lofas page 2 par.3) and GE Healthcare and Karlsson provide a specific sensor chip that has a binding partner having hydrophobic groups. The modified sensor of Lofas in view of GE Healthcare and Karlsson can also be used for determining aggregates because the hydrophobic sensor surface can interact with aggregated molecules better than non-aggregated molecules as taught by Wang.
Conclusion
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/CHAU N.B. TRAN/Examiner, Art Unit 1677
/BAO-THUY L NGUYEN/Supervisory Patent Examiner, Art Unit 1677 March 10, 2026