Abstract

   Current strategies to treat tuberculosis (TB) and co-morbidities
   involve multidrug combination therapies. Rifamycin antibiotics are a
   key component of TB therapy and a common source of drug–drug
   interactions (DDIs) due to induction of drug metabolizing enzymes
   (DMEs). Management of rifamycin DDIs are complex, particularly in
   patients with co-morbidities, and differences in DDI potential between
   rifamycin antibiotics are not well established. DME profiles induced in
   response to tuberculosis antibiotics (rifampin, rifabutin and
   rifapentine) were compared in primary human hepatocytes. We identified
   rifamycin induced DMEs, cytochrome P450 (CYP) 2C8/3A4/3A5, SULT2A, and
   UGT1A4/1A5 and predicted lower DDIs of rifapentine with 58 clinical
   drugs used to treat co-morbidities in TB patients. Transcriptional
   networks and upstream regulator analyses showed FOXA3, HNF4α, NR1I2,
   NR1I3, NR3C1 and RXRα as key transcriptional regulators of rifamycin
   induced DMEs. Our study findings are an important resource to design
   effective medication regimens to treat common co-conditions in TB
   patients.

   Subject terms: Pharmacology, Antimicrobials

Introduction

   Tuberculosis (TB) is a leading cause of morbidity and mortality
   worldwide^[34]1 and antibiotic therapy is a major infection-controlling
   measure in persons with either latent or active TB infection^[35]2.
   Human immunodeificency virus (HIV)-TB co-infected persons are
   susceptible to bacterial, viral, and fungal opportunistic infections
   (OIs) and moreover, cancer co-exist in more than four percent of active
   TB patients, which add to morbidity and mortality^[36]3–[37]8.
   Concurrent treatment of TB, HIV, OIs and cancer often necessitates
   polypharmacy, and requires management of drug-drug interactions (DDI)
   to prevent detrimental treatment outcomes.

   Drug-susceptible TB is currently treated with 6 months of daily
   rifampin therapy, which is combined with other antibiotics such as
   isoniazid, pyrazinamide, and ethambutol^[38]9. Additionally, many of
   the current treatment options for latent tuberculosis infection (LTBI)
   and prophylactic regimens for TB involve the use of rifampin or
   rifapentine^[39]9–[40]11. Rifamycin antbiotics are known to be a common
   cause of DDIs with co-administered medications via induction of drug
   metabolizing enzymes (DMEs). Clinical DDI data guide the management of
   rifamycin DDIs during polypharmacy, but comprehensive clinical DDI data
   are not available across all rifamycin antibiotics to manage common
   medication therapies to treat co-morbidities such as HIV and OIs in TB
   patients^[41]10,[42]11.

   The primary human hepatocyte (PHH) model is a gold standard to unravel
   potential DDIs through expression profiling of genes encoding enzymes
   and transporters that alter drug metabolism^[43]12. In this process,
   the application of highly sensitive next generation sequencing (NGS)
   technology determines changes in extremely low DME transcript copy
   numbers expressed in PHHs to understand their role in metabolism and
   disposition of drugs interacting with rifamycins. Then, transcriptomic
   data obtained in NGS is used in a downstream systems pharmacology
   approach to identify novel targets, which allows better understanding
   of potential DDIs and prediction of effectiveness of a drug regimen
   during co-prescription of medications^[44]13–[45]16.

   Several potential DDIs associated with rifamycins remain unaddressed.
   First, studies utilizing NGS to profile rifamycin responsive genes
   (RGs) to predict clinical DDI outcomes have not been conducted. Second,
   data available on rifampin associated DDIs are complex due to in-vitro
   data generation in alternative hepatic cell line models and seldom in
   PHHs^[46]17. While translatability of these alternative hepatocyte
   models to PHHs is unclear, complexity is further increased when PHHs
   were derived from volunteers who were exposed to alcohol, tobacco, and
   medications which may have a direct influence on DME profiles and
   thereby potential DDI inferences^[47]18,[48]19. Finally, no comparative
   study has been performed on transcriptional responses of rifampin,
   rifabutin, and rifapentine in PHHs to examine and compare independent
   and integrated gene signatures, underlying pathways, networks, and
   metabolic programs, which are key molecular events underlying rifamycin
   mediated DME expression and associated DDIs.

   In response to these key knowledge gaps, we applied NGS in tandem with
   systems pharmacology tools to determine DME expression profiles in
   metabolically active PHHs derived from three healthy
   (drug/tobacco/alcohol free) volunteers in response to rifampin,
   rifabutin, and rifapentine. We have identified integrated
   transcriptional signatures of rifamycin drugs and pathways that
   regulate drug metabolism, networks of genes, and transcriptional
   programs regulating drug metabolism. This information was used to
   predict outcomes of potential DDIs with rifamycins and drugs used to
   treat HIV, cancer, and other common disease states in TB patients.

Results

Rapid metabolism of rifapentine in PHHs among rifamycins

   PHH associated rifamycin intracellular concentrations (geometric mean)
   at 72 h post equimolar treatment were: rifabutin (879.1 ± 211.7, mean
   (ng/million cells) ± standard deviation (SD)), rifampin (519.5 ± 39.4)
   and rifapentine (10.57 ± 1.1) (Fig. [49]1). Metabolite to parent
   (Cm/Cp) ratios of rifampin, rifabutin, and rifapentine to
   25-O-desacetyl rifampin (des-rifampin), 25-O-desacetyl rifabutin
   (des-rifabutin), and 25-O-desacetyl rifapentine (des-rifapentine)
   metabolites were 0.09, 0.10 and 48.93, respectively (Fig. [50]1).
   Results were consistent among three lots of PHHs derived from three
   independent healthy donors who were free of drug, tobacco, alcohol, and
   medicine usage (demographic details are listed in Table [51]S1). These
   results show that in PHHs, rifampin and rifabutin have higher
   intracellular concentrations than rifapentine due to its rapid
   metabolism to antimicrobially active des-rifapentine.

Figure 1.

   [52]Figure 1
   [53]Open in a new tab

   Bioavailability of parent rifampin, rifabutin, and rifapentine drugs
   and their des-metabolites in primary human hepatocytes (PHHs). PHHs
   derived from healthy donors were independently treated with rifamycin
   antibiotics (10 µM) for 72 h. Intracellular concentration of parent to
   metabolites (Cp/Cm) were quantified using liquid chromatography tandem
   mass spectrometry (LC/MS) analysis and drug concentration per million
   PHHs are shown.

Transcriptomic analysis identified integrated gene signatures of rifamycins
in PHHs

   Transcriptomic analysis showed that a number of rifapmin, rifabutin,
   and rifapentine associated transcripts were significantly (p < 0.05)
   altered (> 1.5 fold change [FC]) 619 (1.53%), 1811 (4.47%), and 526
   transcripts (1.3%), respectively in PHHs as compared to vehicle
   (methanol, 0.0025%) treated controls (Figure [54]S1A, S1B and S1C).
   Since rifabutin and rifapentine are structural analogues of rifampin,
   which was originally modified from rifamycin B, we have predicted
   expression of a unique set of integrated transcripts with similar
   expression patterns in all rifamycins or in pairs (rifampin and
   rifabutin, rifabutin and rifapentine, and rifampin and rifapentine). We
   performed a venn diagram analysis and identified 126 transcripts
   (0.31%) that were significantly regulated by all rifamycin drugs and
   constitute an integrated gene signature (Fig. [55]2A and B). A total of
   368 transcripts in rifampin (59.45%) and rifabutin (20.32%), 209 in
   rifabutin (11.54%) and rifapentine (39.73%), and 168 in rifampin
   (27.14%) and rifapentine (31.93%) were altered at the transcriptional
   level in their combinatorial responses in PHHs (Fig. [56]2B).
   Interestingly, a total of 20.3% of total rifampin, 6.9% of rifabutin,
   and 23.9% of rifapentine regulated differentially expressed genes were
   contributing to the integrated rifamycin gene signature (Fig. [57]2B).
   These results showed that < 4.5% of whole transcriptomic changes
   overlap among the rifamycins and that the responsive transcriptomes
   contain a large number of transcripts that are characteristically
   regulated by each rifamycin in PHHs.

Figure 2.

   [58]Figure 2
   [59]Open in a new tab

   Integrated gene signature of rifamycin antibiotics and pathways induced
   in PHHs in response to 10 µM of rifampin (RIF), rifabutin (RFB) and
   rifapentine (RPT) treatment. (A) Rifampin, rifabutin, and rifapentine
   responsive transcripts either uniquely, combinedly, or uniformly
   regulated among rifamycins in PHHs are shown. (B) Heat map shows a
   total of 126 transcripts that are uniformly regulated among rifamycins,
   and transcripts were organized based on their level of mRNA expression.
   (C) Biological and metabolic pathways significantly (< 0.01 p value,
   0.1 ratio) regulated based on a list of up regulated transcripts in
   response to rifampin, rifabutin, and rifapentine in PHHs as compared to
   controls are shown. (D) Interactive transcriptional networks of
   rifampin, rifabutin, and rifapentine responsive genes regulated in PHHs
   following drug treatments. (E) Drug metabolism networks (DMNs) of drug
   metabolizing enzyme (DME) transcripts specifically induced by rifampin,
   (F) rifabutin, and (G) rifapentine built using ingenuity pathway
   analysis software are shown. Red color indicates the up regulation
   of a transcript. Transcription factors regulating the expression of
   drug metabolism genes are shown in the center of the network.

DME profiles in rifamycin responsive PHHs predict lower drug interaction
potential of rifapentine

   Rifamycin responsive PHH transcriptomes consisted of distinct sets of
   DMEs. A total of 19 cytochrome P450 genes (CYPs) were induced by
   rifamycins, and CYP2A1 was the only down regulated CYP transcript post
   rifabutin treatment (Table [60]1). In this analysis, four CYPs, CYP2C8,
   3A4, 3A7, and 3A43, were integral to all rifamycin responses. CYP3A4, a
   key DME known to regulate the metabolism of a large number of drugs,
   followed a FC pattern, rifapentine (6.8) < rifampin (16.7) < rifabutin
   (25.5) (Table [61]1). Among UDP-glucose-glycoprotein
   glucosyltransferases (UGTs), 1A4 and 1A5 were induced by rifamycins,
   UGT1A1 and 1A3 by rifampin and rifabutin, while others were unique to
   individual rifampin and rifapentine treatment (Table [62]1). Among
   non-CYP classes of genes that regulate drug metabolism, rifamycin
   induced ATP binding cassette protein (ABCC) 11, apolipoprotein (APOL)
   3; rifampin and rifabutin induced ATP binding cassette subfamily B,
   member 1 (ABCB1), ABCB4, ABCC6P1, ABCC7, and all others were unique to
   rifamycins (Table [63]S2). Among 56 solute carrier family proteins
   (SLCs), expression of SLC6A12 and 38A4 were induced, and 42A3 was
   down regulated by all rifamycins (Table [64]S2). These data showed that
   rifamycins induced a large number of metabolism-associated genes, but
   only 6.28% (12 out of 191) of transcripts were integral to all
   rifamycins, while others were unique to each of the rifamycin drug
   responses. Expression profiles of CYP3A4 and other DME transcripts
   described above showed that rifapentine may have lower DDI potential
   among rifamycins. Table [65]1 summarizes the FC of associated DME genes
   in response to individual rifamycin drug treatment.

Table 1.

   Drug metabolizing enzymes (DMEs) significantly (> 1.5 fold change
   and < 0.05p value) regulated in response to rifampin (RIF), rifabutin
   (RFB) and rifapentine (RPT) at 10 μM in primary human hepatocytes
   (PHHs) as compared to vehicle (methanol, 0.0025%) treated controls.
   NCBI gene IDs are shown under the column ID#.
   Gene RIF RFB RPT ID# Gene RIF RFB RPT ID# Gene RIF RFB RPT ID# Gene RIF
   RFB RPT ID#
   Drug metabolizing enzymes (DMEs)
   I. Oxidative Phase I enzymes B. Monoamine oxidases (MAOs) H.
   Glutathione peroxidases (GPXs) N. Alkaline phosphatases (ALPs)
   A. Cytochrome P 450 enzymes (CYPs) MAOB 1.7 4,129 GPX2 2.1 2,877 ALPI
   15.6 248
   CYP2A1 − 1.7 1543 C. Aldehyde dehydrogenases (ALDHs) I.
   Carboxypeptidases ALPL 2.4 249
   CYP2A6 3.5 5.2 1548 ALDH1A2 2.0 8,854 SCPEP1 1.8 1.5 59,342 O.
   N-acetyltransferases (NATs)
   CYP2A7 2.6 5.0 1549 ALDH1L1 4.7 3.1 10,840 CPA4 − 3.2 51,200 NAA15
   − 1.5 80,155
   CYP2A13 4.2 5.9 1553 ALDH3A1 1.7 218 CPO 1.7 130,749 SAT2 1.5 112,483
   CYP2B6 6.1 3.7 1555 ALDH5A1 1.5 7,915 CPVL 1.6 1.9 54,504 P. Amino acid
   conjugating enzymes
   CYP2B7P 3.6 3.5 5.1 1556 ALDH6A1 1.8 4,329 J. Endopeptidases ACSL4
   − 2.2 2,182
   CYP2C8 13.5 18.1 1558 ALDH8A1 2.4 64,577 PHEX 1.5 5,251 ACSL6 1.8
   23,305
   CYP2C9  2.7 4.3 1559 II. Reductive phase I DMEs IV. Conjugative phase
   II DMEs ACSM1 3.1 116,285
   CYP2C19 1.8 1557 D. Aldo–keto reductases (AKRs) K. UDP glucuronosyl
   transferases (UGTs) ACSM2A 1.8 3.1 123,876
   CYP2S1 2.0 29,785 AKR1B1 1.9 2.7 231 UGT1A1 2.5 2.9 54,658 ACSM2B 2.8
   348,158
   CYP2W1 2.1 54,905 AKR1D1 4.5 2.2 2.2 6,718 UGT1A3 1.8 2.0 54,659 ACSM3
   2.4 6,296
   CYP3A4 16.7 25.5 6.8  1576 AKR1A1 1.5 10,327 UGT1A4 3.6 3.5 1.5 54,657
   ACSS1 2.1 84,532
   CYP3A5 2.6 3.7 1577 AKR1B10 − 3.7 57,016 UGT1A5 6.2 5.5 2.1 54,579
   GLYAT 3.1 10,249
   CYP3A7 6.2 7.3  3.3 1551 AKR1B15 − 2.5 − 3.0 441,282 UGT1A8 1.8 54,576
   LPCAT4 − 1.6 254,531
   CYP3A43 5.8 9.1  3.1 64,816 AKR1C6P 3.1 389,932 UGT1A9 1.6 54,600 LRAT
   1.9 9,227
   CYP4V2 1.7 285,440 AKR7A2P1 1.7 246,182 UGT1A10 1.5 54,575 Q. Methyl
   Transferases (MTs)
   CYP7A1 9.4 1581 E. Quinone reductases UGT2B4 1.6 7,363 AS3MT 1.5 57,412
   CYP7B1 1.6 9,420 NQO1 − 2.5 1728 UGT2B15 2.2 7,366 BHMT 3.4 1.6 635
   CYP11A1 1.8 1.7  1583 III. Hydrolytic Phase I DMEs UGT2B17 2.3 7,367
   DOT1L − 1.5 84,444
   CYP21A1P 1.5 1,590 F. Epoxide hydrolases (EPHXs) L. Sulfotransferases
   (SULTs)
   Other Cytochrome transcripts EPHX1 2.7 3.2 1.7 2052 SULT2A1 2.5 3.8 2.1
   6,822 HNMT 1.6 3,176
   CYBA  2.2 1535 G. Aminopeptidases SULT4A1 2.0 25,830 METTL7A 1.5 25,840
   CYB5A 2.2 1.6 1528 ENPEP 1.9 2028 M. Glutathione S-transferases (GSTs)
   METTL7B − 1.5 196,410
   CYB5D2 1.5 124,936 ERAP1 1.7 51,752 GSTM2 1.7 2.0 2,946 NNMT − 2.3
   4,837
   CYB5RL 1.7 606,495 ASPRV1 1.8 151,516 GSTT2B 1.7 653,689 TRMT10A − 1.6
   93,587
   CYBRD1  1.9 79,901 MASP1 1.5 2.5 5,648
   [66]Open in a new tab

Lower interaction potential of rifapentine with therapeutics used to treat
HIV, cancer and other co-morbidities in TB patients

   One of the major objectives of our study was to identify potential
   interactions of rifamycins with drugs used to treat common
   comorbidities in TB patients. In this process, DME expression profiles
   from the list of rifamycin-RGs were an important resource to predict
   clinical DDIs. The most important qualitative features of NGS are (i)
   sequence accuracy and (ii) gene features, including, coding (exon),
   non-coding (intron), intergenic, and untranslated regions. RNA profiles
   of our study samples showed near to 100% perfect index reads and
   maintained similar exon and intron sequences among replicates of
   rifampin, rifabutin, rifapentine treated and control (methanol and
   untreated) samples (Figures [67]S2 and [68]S3), which confirmed their
   quality and improved confidence in our data. With the disease and
   function annotation analysis tool in ingenuity pathway analysis
   software, we predicted the interaction of rifamyins with 58
   FDA-approved drugs. Among these drugs, 17 were used to treat HIV, 10 to
   treat cancer, one to treat malaria and two to treat fungal infections
   (Table [69]2). Among anti-HIV drugs, protease inhibitors, including,
   atazanavir, darunavir, fosamprenavir, lopinavir, saquinavir,
   tipranavir, and the non-nucleoside reverse transcriptase inhibitor,
   rilpivirine, and pharmacokinetic enhancers, cobicistat and ritonavir,
   are all readily metabolized by hepatic CYP3A4 (Table [70]2). Based on
   CYP3A4 regulation, we have identified a comparative DDI pattern of
   rifapentine < rifampin < rifabutin (Table [71]2). Rifampin and
   rifabutin induced UGT1A1, which is involved in raltegravir and abacavir
   metabolism (Table [72]2) with a comparative DDI pattern,
   rifapentine < rifampin < rifabutin. Multiple DMEs are involved in the
   major metabolism of many antiretroviral drugs, including dolutegravir,
   elvitegravir, efavirenz, etravirine, and nevirapine (Table [73]2),
   based on deducibility, rifapentine may have lower drug interaction
   potential as compared to rifampin and rifabutin when used in
   combination with these antiretrovirals. Disease and function annotation
   analysis predicted interaction of rifamycins with other drugs used to
   treat multiple diseases in TB patients (Table [74]3). These data
   summarize the DDI potential of rifamycins and indicate a lower
   interaction potential of rifapentine among rifamycins.

Table 2.

   Predicted drug-drug interaction (DDI) potential of rifamycin
   antibiotics with therapeutics used to treat HIV, fungal, malarial
   parasitic infections and cancer in tuberculosis patients.
   Interacting drugs Drug metabolizing enzymes induced during treatment
   DDI pattern
   Rifampin (RIF) Rifabutin (RFB) Rifapentine (RPT)
   Antiretroviral Drugs (ARVs)
   ARV Category I: Integrase Strand Transfer Inhibitors (INSTIs)
   Dolutegravir CYP3A4, UGT1A1 CYP3A4, UGT1A1 CYP3A4 RPT < RIF < RFB
   Bictegravir CYP3A4, UGT1A1 CYP3A4, UGT1A1 CYP3A4 RPT < RIF < RFB
   Elvitegravir CYP3A4, UGT1A1, UGT1A3 CYP3A4, UGT1A1, UGT1A3 CYP3A4
   RPT < RIF < RFB
   Raltegravir UGT1A1 UGT1A1 RPT < RIF < RFB
   ARV Category II: Non-nucleoside Reverse Transcriptase Inhibitors
   (NNRTIs)
   Efavirenz CYP2A6, 2B6 CYP2A6, 2B6, 2C19 RPT < RIF < RFB
   Etravirine CYP3A4, 2C9 CYP3A4, 2C9, 2C19 CYP3A4 RPT < RIF < RFB
   Nevirapine CYP3A4, 2B6 CYP3A4, 2B6 CYP3A4 RPT < RIF/RFB
   Rilpivirine CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   ARV Category III: Nucleoside Reverse Transcriptase Inhibitors (NRTIs)
   Abacavir UGT1A1 UGT1A1 RPT < RIF < RFB
   ARV Category IV: Protease Inhibitors (PIs)
   Atazanavir CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Darunavir CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Fosamprinavir CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Lopinavir CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Saquinavir CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Tipranavir CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   ARV Category IV: Entry Inhibitors (EIs)
   Maraviroc CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   ARV Category V: Pharmacokinetic enhancers (PK boosters)
   Cobicistat CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Ritonavir CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Antifungals
   Terbinafine CYP2B6, 2C8, 2C9, 3A4, 3A5 CYP2B6, 2C19, 2C8, 2C9, 3A4, 3A5
   CYP2C8, 3A4 RPT < RIF < RFB
   Voriconazole CYP2C9,CYP3A5 CYP2C9,CYP2C19,CYP3A5 - RPT < RIF < RFB
   Antimalarials
   Quinine CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Anticancer Drugs
   Beta-estradiol CYP3A4, 3A5, 3A7 CYP3A4, 3A5, 3A7 CYP3A4, 3A7
   RPT < RIF < RFB
   Cyclophosphamide CYP2A6, 2B6, 2C8, 2C9, 3A4 CYP2A6, 2B6, 2C19, 2C8,
   2C9, 3A4 CYP2C8, 3A4 RPT < RIF & RFB
   Docetaxel CYP3A4, 3A5 CYP3A4, 3A5 CYP3A4 RPT < RIF < RFB
   Etoposide CYP2C9, 3A4, 3A5 CYP2C9, 3A4, 3A5 CYP3A4 RPT < RIF < RFB
   Ifosfamide CYP2A6, 2B6, 2C8, 2C9, 3A4 CYP2A6, 2B6, 2C19, 2C8, 2C9, 3A4
   CYP2C8, 3A4 RPT < RIF & RFB
   Omeprazole CYP2A6, 2C9, 3A4 CYP2A6, 2C19, 2C9, 3A4 CYP3A4
   RPT < RIF < RFB
   Paclitaxel CYP2B6, 2C8, 3A4, 3A5 CYP2B6, 2C8, 3A4, 3A5 CYP2C8, 3A4
   RPT < RIF & RFB
   Tamoxifen CYB5A, CYP2B6, 2C9, 3A4, 3A5; UGT2B15 CYP5A, 2B6, 2C19, 2C9,
   3A4, 3A5 CYP3A4 RPT < RIF & RFB
   Thalidomide CYP2C9, 3A5 CYP3A5 RPT < RIF < RFB
   Tretinoin CYP2B6, 2C8, 2C9, 3A4, 3A5, 3A7 CYP2B6, 2C8, 2C9, 3A4, 3A5,
   3A7, RDH16, ALDH8A1 CYP2C8, 2S1, 3A4,3A7 ND
   [75]Open in a new tab

   Abbreviations: CYP, cytochrome P450; UGT, UDP glucuronosyl transferase;
   RDH16, Retinol dehydrogenase 16 and ALDH8A1, aldehyde dehydrogenase 8
   family member A1 and ND, not determined.

Table 3.

   Predicted Drug-Drug Interactions (DDIs) of rifamycins with therapeutics
   used to treat various illnesses in TB patients.
   Interacting drugs Drug metabolizing enzymes induced during treatment
   Predicted DDI pattern
   Rifampin (RIF) Rifabutin (RFB) Rifapentine (RPT)
   Anti-inflammatory drugs
   Indomethacin - CYP2C19, CYP2C9 - RPT < RIF < RFB
   Colchicine CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Anti-diabetic drugs
   Tolbutamide CYP2C8, 2C9, 3A5 CYP2C8, 2C9, 2C19, 3A5 CYP2C8
   RPT < RIF < RFB
   Immunosuppressive drugs
   Sirolimus CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Antiarrhythmic drugs
   Amiodarone CYP2C8, 3A4, 3A5 CYP2C8, 2C19, 3A4, 3A5 CYP2C8, 3A4
   RPT < RIF < RFB
   Lidocaine CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Antihypertensive drugs
   Verapamil CYP2C8, 3A4, 3A5 CYP2C8, 3A4, 3A5 CYP2C8, 3A4 RPT < RIF < RFB
   Antidepressant drugs
   Bupropion CYP2B6, 3A4 CYP2B6, 3A4 CYP3A4 RPT < RIF < RFB
   Imipramine CYP2B6, 3A4, UGT1A4 CYP2B6, 2C19, 3A4, UGT1A4 CYP3A4, UGT1A4
   RPT < RIF & RFB
   Morphine CYP2C8, 3A4 CYP2C8, 3A4 CYP2C8, 3A4 RPT < RIF < RFB
   Anticonvulsant drugs
   Carbamazepine CYP2C8, 3A4, 3A5, 3A7 CYP2C8, 3A4, 3A5, 3A7 CYP2C8, 3A4,
   3A7 RPT < RIF < RFB
   Antipsychotic drugs
   Haloperidol CYP3A4, 3A5 CYP3A4 CYP3A4 RPT < RIF < RFB
   Antisedative drugs
   Midazolam CYP2B6, 3A4, 3A5 CYP2B6, 3A4, 3A5 CYP3A4 RPT < RIF & RFB
   Triazolam CYP3A4, 3A5 CYP3A4, 3A5 CYP3A4 RPT < RIF < RFB
   Alprazolam CYP3A4, 3A5 CYP3A4, 3A5 CYP3A4 RPT < RIF < RFB
   Diazepam CYP2B6, 2C9, 3A4, 3A5 CYP2B6, 2C19, 2C9, 3A4, 3A5 CYP3A4
   RPT < RIF & RFB
   Vasodilators
   Sildenafil CYP2C9, 3A4 CYP2C9,CYP3A4 CYP3A4 RPT < RIF < RFB
   Analgesics
   Methadone CYP2B6, 2C8, 2C9, 3A4 CYP2B6, 2C8, 2C9, 2C19, 3A4 CYP2C8, 3A4
   RPT < RIF & RFB
   Ibuprofen CYP2C8, 2C9 CYP2C8, 2C9, 2C19 CYP2C8 RPT < RIF < RFB
   Sedatives
   Midazolam CYP2B6, 3A4, 3A5 CYP2B6, 3A4, 3A5 CYP3A4 RPT < RIF/RFB
   Triazolam CYP3A4, 3A5 CYP3A4, 3A5 CYP3A4 RPT < RIF < RFB
   Alprazolam CYP3A4, 3A5 CYP3A4, 3A5 CYP3A4 RPT < RIF < RFB
   Diazepam CYP2B6, 2C9, 3A4, 3A5 CYP2B6, 2C19, 2C9, 3A4, 3A5 CYP3A4
   RPT < RIF & RFB
   Orexogenic drugs
   Dronabinol CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Adrenocortical insufficiency treating drugs
   Hydrocortisone CYP3A4, 3A5, FOXA1 CYP3A4 CYP3A4 RPT < RIF < RFB
   Lipid-lowering drugs
   Fluvastatin CYP2C8, 2C9, 3A4 CYP2C8, 2C9, 3A4 CYP2C8, 3A4
   RPT < RIF < RFB
   Simvastatin CYP2C9, 3A4 CYP2C9, 3A4 CYP3A4 RPT < RIF < RFB
   Pravastatin CYP3A4 CYP3A4 CYP3A4 RPT < RIF < RFB
   Hormonal drugs
   Testosterone CYP3A4, 3A5 CYP3A4 CYP3A4 RPT < RIF < RFB
   Generalized anxiety disorder (GAD) treated drugs
   Escitalopram CYP3A4 CYP2C19, 3A4 CYP3A4 RPT < RIF < RFB
   Smoking cessation aiding drugs
   Nicotine CYP2A6, 2B6 CYP2A6, 2B6 RPT < RIF & RFB
   [76]Open in a new tab

   CYP, cytochrome P450 and UGT, UDP glucuronosyl transferase.

Rifamycins are strongest but selective stimulants of drug metabolism pathways
in PHHs

   A total of 327, 441, and 297 pathways were induced in response to
   rifampin, rifabutin, and rifapentine treatment in PHHs respectively. Of
   these, activated biological pathways, 9 (2.75%), 13 (2.94%), and 7
   (2.35%) were found to be significant at p = 0.01, 0.1 ratio and 2.0 Z
   score. In this, seven pathways were stimulated by all rifamycins
   (Fig. [77]2C). Interestingly, several of the activated drug
   metabolizing pathways involved CYP2C8, 3A4, 3A7, UGT1A4, 1A5, and
   sulfotransferase family 2A member 1 (SULT2A1) DMEs as key modulators
   (Table [78]S3 and [79]S4), which are also part of the integrated
   rifamycin transcriptional signature (Fig. [80]2B and Table [81]S3).
   While rifampin-RGs independently regulated the serotonin degradation
   pathway, rifabutin-RGs stimulated several metabolic pathways, such as
   degradation of glycine, betaine, and methionine and bile acids
   biosynthesis as shown in Table [82]S4. Even in down regulated pathways,
   rifabutin inhibited 9 cell signaling and 2 metabolic pathways,
   including geranyl diphosphate and cholesterol biosynthesis (Figure
   [83]S4 and Table [84]S5). Rifampin and rifapentine did not
   significantly inhibit any pathways and showed their distinction from
   rifabutin regulatory transcriptional pathways. These results showed
   that rifamycins strongly stimulate drug metabolic pathways in PHHs
   involving CYP2C8, 3A4, 3A7, UGT1A4, 1A5, and SULT2A1 as key regulators.

Interactive transcriptional drug metabolism networks predict the role of
NCOA, NF-KB, NR1I2, NR1I3 and RXRα transcription factors in regulation of
DMEs in rifamycin responsive PHHs

   A tightly regulated gene network is dedicated to perform a specific
   function in drug responsive cellular transcriptomes. From a network
   analysis of rifampin, rifabutin, and rifapentine regulatory DME
   profiles, we found 23 rifampin, 25 rifabutin, and 19 rifapentine gene
   networks are operated in PHHs following their independent treatments
   (Fig. [85]2D). In rifamycin responsive gene networks, 1 through 18
   rifampin, all of rifabutin, and 1 through 14 rifapentine regulated
   networks were dedicated to cell signaling and drug metabolism
   functions. Among transcriptomic networks, rifampin, rifabutin, and
   rifapentine responsive 2nd, 6th, and 10th networks were specifically
   associated with drug metabolism.

   These drug metabolism networks (DMNs) involve ≤ 24 DMEs; CYP2C8, 3A4,
   and 3A7; UGT1A4 and 1A5 were integral to these networks
   (Fig. [86]2E–G). No common transcripts were found in either rifabutin
   and rifapentine or rifampin and rifapentine combinedly regulated
   networks. However, both rifampin and rifabutin regulated DMEs,
   including, CYP2A6, 2B6, 2C9, 3A5, UGT1A1, 1A5, and P2RX3 in DMNs
   (Fig. [87]2E and F). These data demonstrate the distinction of
   rifapentine from rifampin and rifabutin functional responses in PHHs
   and also suggest rifapentine may have lower DDI potential with drugs
   metabolized by these transcripts. Along with more than one rifamycin
   antibiotic regulated genes, drug metabolizing networks had a set of
   genes that were unique to each of the rifamycin drugs in their DMNs
   (Fig. [88]2E–G). DMNs have provided valuable data on rifamycin
   regulated NCOA- NR1i2/NR1i3-RXRα; RXR; NF-KB; NR1I2L-NR1I2-RA-RXRα
   transcription factor (TF) axes controlling the expression of downstream
   DME targets involved in drug metabolism (Fig. [89]2E–G). Along with
   above results, rifampin and rifabutin together controlled DMN through
   downstream NR1I3L-NR1I3-RA-RXRα TF axis, and rifabutin regulated DMN
   was specifically controlled by NCOR-LXR-Oxysterol-RXR-9-Cis RA TF axis
   (Fig. [90]2E, F). These data conclude that more than 14 rifamycin
   responsive transcriptional networks are operated by key TFs, NR1I2,
   NR1I3 (NR1I3), NCOA, RXRα and NF-KB, which control DME expression in
   DMNs of rifamycin responsive PHH transcriptomes.

Upstream regulator analysis of rifamycin responsive genes predict FOXA3,
HNF4α, NR1I2, NR1I3, NR3C1, and RXRα as key regulators of metabolic programs
in PHHs

   Further validation of TF axes involving NCOA, NF-KB, NR1I2, NR1I3 and
   RXRα was performed using the upstream regulator analysis (URA) tool in
   IPA. In this analysis, we allocated rifamycin-RGs containing DMEs and
   drug transporters as target genes of TFs, which may control their mRNA
   expression. URA predicted FOXA3, HNF4α, NR1I2, NR1I3, NR3C1, and RXRα
   TFs are potentially involved (< 0.05 p and > 2.0 z score), and each
   regulated more than 10 DMEs and drug transporter targets from the list
   of rifamycin-RGs (Table [91]4 and Table [92]S6). Based on z (> 1.0) and
   p value (< 0.01) based measures, all rifamycin drugs significantly
   activated rifamycin-RGs controlled by NR1I3, whereas both rifampin and
   rifabutin-RGs were targets of HNF4α, NR1I2, NR3C1, and RXRα and showed
   higher specificity to control rifabutin-RG targets (Table [93]4 and
   Table [94]S6). NR1I3 heterodimerizes with RXRα and regulates a distinct
   set of metabolic genes in hepatocytes. In our dataset, we found 20
   rifamycin-RGs as NR1I3 targets and 1 (5%) was induced by rifampin, 5
   (25%) by rifabutin, 8 (40%) by both rifampin and rifabutin, 1 (5%) by
   both rifabutin and rifapentine, and 5 (25%) by all rifamycins (Table
   [95]4 and Table [96]S6). In the liver, HNF4α and NR1I2 are specifically
   expressed to higher levels. In our dataset, HNF4α was predicted to
   regulate 145 rifamycin-RG targets, and in this analysis 10.3% rifampin
   (15 genes), 56.5% rifabutin (82 genes), and 9.6% rifapentine (14 genes)
   were specific to individual rifamycins, and 13.1% to rifampin and
   rifabutin (19 genes), 2.7% to rifabutin and rifapentine (4 genes), and
   7.5% to all rifamycins (11 genes) (Table [97]S6). Notably, the NR1I2
   receptor bound to rifampin, rifabutin, and rifapentine and induce
   expression of its target genes. A total of 22 rifamycin-RGs were
   identified as NR1I2 targets in our dataset, including 10 regulated by
   rifabutin (45.4%), 7 by both rifampin & rifabutin (31.8%), 1 by both
   rifabutin and rifapentine (4.5%), and 5 by all rifamycins (22.7%)
   (Table [98]S6). NR1I2 heterodimerizes with RXRα to regulate expression
   of a unique set of metabolic genes. In a total of 34 RXRα’s
   rifamycin-RG targets, 5 were regulated by rifampin (14.7%), 12 by
   rifabutin (35.2%), 4 by rifapentine (11.7%), 10 by both rifampin and
   rifabutin (29.4%), and 3 (8.8%) by all rifamycins (Table [99]S6).
   Induction of NR3C1 TF was predicted based on it’s association with 55
   targets in URA. In this analysis, 8 genes (14.5%) were regulated by
   rifampin, 24 (43.6%) by rifabutin, 7 (12.7%) by rifapentine, 7 (12.7%)
   by both rifampin and rifabutin, 7 (12.7%) by all rifamycins, and 1
   (1.8%) by rifampin & rifapentine (Table [100]S6). CYP2C8, 3A4, and 3A8
   were key DMEs that metabolized a wider array of drugs that are
   controlled by multiple TFs, including, FOXA3, HNF4α, NR1I2, NR1I3, and
   NR3C1 (Table [101]S6). In summary, URA results showed that HNF4α,
   NR1I2, NR1I3, FOXA3, NR3C1, and RXRα are key TFs controlling expression
   of key metabolic genes, including CYP3A4.

Table 4.

   Transcription factors (TFs) predicted to control the expression of
   target gens induced during rifampin (RIF), rifabutin (RFB) and
   rifapentine (RPT) treatment in hepatocytes that further regulate drug
   metabolism networks were identified by upstream regulator analysis
   tool in IPA software.
   TFs p Value Z Score Status p Value Z Score Status p Value Z Score
   Status
   FOXA3 1.21E−05 2.216 Activated 3.78E−06 1.678 – 6.87E−03 − –
   HNF4α 6.01E−04 2.242 Activated 5.42E−08 4.086 Activated 1.56E−02 0.963
   –
   NR1I2 2.35E−07 3.542 Activated 5.00E−10 2.471 Activated 5.33E−03 1.768
   –
   NR1I3 9.63E−12 3.678 Activated 1.88E−10 3.396 Activated 6.41E−04 2.39
   Active
   NR3C1 2.07E−05 1.982 – 8.21E−04 2.376 Activated 4.06E−03 0.348 –
   RXRα 3.21E−08 1.451 – 4.53E−06 2.415 Activated 6.29E−03 – –
   [102]Open in a new tab

Discussion

   Previous in vitro approaches to examine the DDI potential of rifamycins
   have either used real time polymerase chain reaction (RT-PCR) or
   microarray technologies to quantify changes in the gene expression of
   DMEs^[103]16,[104]20–[105]24. The results from these studies have
   provided some evidence on DME expression pattern in PHHs, and may
   differ from a highly sensitive NGS approach^[106]25. Moreover, existing
   evidence on rifabutin and rifapentine DDI potential is limited. We
   overcame several of these challenges with the use of (1) PHHs from
   healthy donors free of drug, tobacco, alcohol, and medicine usage
   (Table [107]S1); (2) the use of NGS technology to precisely quantitate
   changes in gene expression; and (3) comparison of rifampin, rifabutin,
   and rifapentine on a similar platform using equimolar drug treatment
   concentrations (10 μM) to demonstrate comparative DME gene expression.

   Deciphering transcriptome based metabolic responses to a drug in PHHs
   is an important “in vitro” strategy to identify potential DDIs. In
   transcriptomic responses, changes in DMEs and drug transporter patterns
   further influence metabolic pathways, interactive transcriptional
   networks, and upstream regulators, all of which provide significant
   information on potential drug interactions and molecular events
   underlying DME profiles and their associated DDIs. In our experiments,
   the initial quantitation of intracellular concentrations of rifampin,
   rifabutin, and rifapentine and their active desacetyl-metabolites in
   PHHs has demonstrated the relative metabolic rates of rifamycins in
   PHHs. We found that the metabolic rates followed a pattern of
   rifapentine > rifabutin ≥ rifampin, which formed an important basis for
   downstream transcriptomic studies.

   Both rifapentine and 25-desacetyl-rifapentine have antimicrobial
   properties, which may contribute to its prolonged antimicrobial
   activity^[108]26, and its therapeutic effectiveness has been observed
   in recent clinical studies^[109]27-[110]29. While rifamycins share a
   structure–activity relationship among themselves, there are some
   considerable differences observed in their antimicrobial and other
   pharmacologic properties: (1) rifabutin has enhanced antimicrobial
   activity against Mycobacterium avium and its potency was found to be
   similar to rifampin against M. tuberculosis^[111]30; (2) the half-life
   (t1/2) of rifabutin (32–67 h) > rifapentine (14–18 h) > rifampin
   (2–5 h) in the serum of treated TB patients^[112]31,[113]32 with the
   relative protein binding ability of rifapentine (97.7%) > rifampin
   (≤ 88%) > rifabutin (85%)^[114]33–[115]35; (3) maximal concentrations
   (C[max]) of rifamycins observed in the serum of rifamycin treated TB
   patients follow a rifapentine (≤ 30 mg/L, 600 mg single daily
   dose) ≥ rifampin (≤ 20 mg/L, 600 mg) > rifabutin (≤ 0.6 mg/L, 300 mg)
   pattern with the clinically used doses^[116]32 and (4) all rifamycins
   undergo deacetylation and form ‘deacetyl’ derivatives, but rifampin and
   rifapentine uniquely undergo hydrolysis and form ‘formyl’ derivatives,
   whereas rifabutin undergoes hydroxylation and forms ‘hydroxyl’
   derivatives. A comparison of transcripts induced in response to each of
   the rifamycins in PHHs has not been identified to date. This study
   showed that 126 transcripts were integrally regulated by all
   rifamycins, and rifampin, rifabutin, and rifapentine contributed 1.53%,
   4.47%, and 1.3% of total transcripts in PHHs, respectively
   (Fig. [117]2A). About 1.22%, 4.15%, and 0.99% of total transcripts were
   uniquely regulated in response to rifampin, rifabutin, and rifapentine
   responses in PHHs, respectively. Relatedly, several metabolic pathways
   were significantly induced by all rifamycins.

   In drug metabolism, CYP3A4 is a key common metabolic enzyme involved in
   clearance of > 80% of currently used therapeutics^[118]36. This is the
   first RNA sequence-based report showing the CYP3A4 induction pattern
   among rifamycins as rifapentine < rifampin < rifabutin (Table [119]1).
   Previously, Dooley et al.^[120]37 showed that when midazolam was
   combined with rifapentine, midazolam had faster clearance rate as
   compared to midazolam combined with rifampin, potentially due to higher
   CYP3A4 activity. In contrast, Li et al., showed that comparative
   induction of CYP3A4 acvtivity among rifamycins was
   rifampin > rifapentine > rifabutin based on 6ß- hydroxylation of
   testosterone as an indirect measure of CYP3A4 activity^[121]21. Later,
   Williamson et al. showed a different pattern of
   rifampin > rifabutin > rifapentine based on in vitro real-time PCR
   experiments. Our data are consistent with the report from Williamson et
   al.^[122]23, which showed that rifapentine was identified as the
   weakest CYP3A4 inducer among rifamycins on an equimolar basis. Whether
   differences in the level of absorption of rifamycin antibiotics in the
   gut of TB infected persons contribute to the different CYP3A4 induction
   pattern of rifamycin drugs in “in vivo” studies is unclear.

   Consistent with earlier reports, metabolism of
   melatonin^[123]38,[124]39 and bupropion^[125]40 were increased, along
   with acetone and nicotine metabolism, based on rifampin mediated DME
   expression (Table [126]S3). We found rifapentine to be the weakest
   inducer of CYP2C8, 3A4, 3A7, and UGT1A4 and 1A5 as compared to rifampin
   and rifabutin at similar concentrations (10 µM). A previous clinical
   study by Burman et al.^[127]32 showed that the use of a CYP3A4
   inhibitor enhanced the bioavailability of rifabutin, whereas the
   inhibitor could not boost rifampin and rifapentine bioavailability.
   Based on our study results, rifabutin was the highest inducer of CYP3A4
   and is a major pathway involved in its clearance and thereby it’s
   inhibition may have increased it’s bioavailability.

   Healthy volunteer studies of the antiretroviral drug raltegravir in
   combination with rifapentine, rifampin or rifabutin are consistent with
   our findings. Combination of raltegravir (400 mg twice daily) with
   rifapentine (600 mg once daily) showed an decrease in raltegravir’s AUC
   0–12 h by 5% as compared to raltegravir alone^[128]41. In contrast,
   both rifampin (600 mg single daily dose) and rifabutin (300 mg single
   daily dose) reduced raltegravir AUC 0–12 h by 41% and 19%
   respectively^[129]42,[130]43. However, regimens used to either treat or
   prevent TB often utilize multiple drugs in combination, such as
   rifampin or rifapentine in combination with isoniazid, making
   interpretation of possible drug-drug interactions difficult. More so,
   differences exist in the adminsitration of rifamycins between regimens,
   including various doses and dosing schedules. Collectively, these
   differences hinder the ability to directly compare regimens and their
   effects on DMEs.

   Unlike rifampin and rifabutin, rifapentine may not influence a large
   number of metabolic pathways as it showed a higher specificity among
   rifamycins. Rifapentine is combined with isoniazid for treatment of
   LTBI and a one month regimen was shown non-inferior to nine months of
   isoniazid or four months of rifampin or three months of rifapentine and
   isoniazid therapy^[131]44. In the case of DME inducing ability, all
   rifamycins induced UGT1A4, 1A5, and SULT2A1. In addition, rifampin and
   rifabutin combinedly induced UGT1A1 and 1A3, while rifampin induced
   UGT2B4, 2B17, and 2B15, which indicated their additional influence with
   potential drug metabolizing pathways as compared to rifapentine (Table
   [132]S3).

   The specific metabolic programs influenced by treatment of PHHs with
   rifamycins were evident through analysis of interactive transcriptional
   networks. In our analysis, we identified NF-KB, NCOA, NR1I2, NR1I3 and
   RXRα TFs as key regulators of a complex network of DMEs and drug
   transporters. Additional evidence from URA showed the involvement of
   FOXA3, HNF4α, NR1I2, NR1I3, NR3C1 and RXRα TFs on regulation of CYPs
   and other key metabolic genes in PHHs. The role of FOXA3 and NR3C1 on
   controlling the expression of DMEs in PHHs has largely remains
   unexplored. Though this study has provided a strong evidence on the
   regulation of above TFs in controlling the expression of DMEs during
   rifamycin treatment, additional evidence supporting our data may
   strengthen these findings and fill in the important gaps in DDIs that
   arise with the use of rifamycins. Though this study has discussed
   consistency of rifamycin DDI pattern with clinical data on rifamycins
   and their interactions with raltegravir, additional supportive clinical
   data may be needed on rifamycin DDIs with various drugs used to treat
   co-morbidities in TB patients. Future studies may provide additional
   evidence to show clinical significance of effective therapies designed
   based on our study to treat co-morbidities in TB patients.

   In conclusion, our data has provided evidence on relative changes in
   DMEs and drug transporter profiles altered in response to rifampin,
   rifabutin, and rifapentine treatment. Additionally, this study
   illustrated the lower interaction potential of rifapentine among
   rifamycins with concomitant medications such as antiretroviral and
   anticancer drugs. Finally, it confirms the involvement of FOXA3, HNF4α,
   NR1I2, NR1I3, NR3C1 and RXRα TFs in regulation of DMEs including
   CYP3A4.

Materials and methods

Primary human hepatocytes (PHHs)

   Human plateable induction-qualified PHHs isolated from three
   drug/tobacco/alcohol free healthy donors were purchased from Life
   technologies corporation (Chicago, IL) or Lonza (Chicago, IL)
   (additional details described in Table [133]S1). Reagents required to
   culture PHHs were purchased from Life technologies corporation,
   Chicago, IL unless otherwise noted. Cells were thawed at 37 °C for one
   minute and were transferred into 50 mL of hepatocyte thawing medium
   (CAT# CM7500) and the tube was centrifuged at 100 g for 10 min. PHHs
   were seeded at 0.5 million cells per well density in a collagen coated
   (12–18 h) six well plate (CAT#A1142802). PHHs were then cultured in
   Williams E medium without phenol red (CAT#A1217601) supplemented with
   Hepatocyte thawing and plating supplements (CAT#CM3000) and incubated
   at 37 °C. Medium was replaced with Williams E medium without phenol red
   supplemented with Hepatocyte maintenance cocktail (CAT#CM4000) and were
   incubated for 48 h with daily changes of culture medium before drug
   treatments.

Drugs, treatments and drug analysis

   Rifampin and rifabutin were purchased from US Pharmaceuticals,
   Rockville, MD and rifapentine was a kind gift of Sanofi-Aventis, Paris,
   France. PHHs were treated with rifampin, rifabutin and rifapentine at
   10 µM concentration for 72 h. with the corresponding untreated
   (negative) and 0.0025% methanol (vehicle) treated controls. We measured
   intracellular concentrations (ICC) of parent and des-acetyl-metabolites
   by liquid chromatography tandem mass spectroscopy (LC–MS/MS) as we have
   previously described^[134]45–[135]49.

RNA Isolation, NGS and differential gene expression analysis

   Total RNA, was extracted from rifampin, rifabutin, rifapentine and
   vehicle treated and untreated PHHs using RNeasy fibrous tissue mini kit
   (Qiagen, Carol Stream, IL) according to the manufacturer’s protocol and
   as described previously^[136]51,[137]52. To quantify copies of
   transcripts expressed in PHHs in response to rifampin, rifabutin and
   rifapentine, we performed NGS using the Illumina HiSeq 2500 sequencer
   available at the University of Nebraska Medical center (UNMC)’s
   genomics core facility at UNMC, Omaha, NE. Complimentary DNA (cDNA)
   libraries for rifamycin treated PHH samples were constructed using the
   TruSeq RNA Library Preparation Kit (Illumina, San Diego, CA). All
   samples were subjected to 50-cycle, single-read sequencing in the
   HiSeq2500 and were demultiplexed using Bcl2Fastq v2.17.1.14 (Illumina,
   San Diego, CA). Short cDNA fragment data were compiled in FASTQ format.
   Further analysis of FASTQ files were performed in strand NGS software
   (Agilent technologies, USA). Gene expression levels were calculated
   using fragments per kilobase of transcript per million mapped reads
   (FPKM) following normalization. RNA sequencing data files and processed
   transcript expression are available at NCBI GEO (Accession No#
   [138]GSE139896). The data obtained from sequence analysis were of high
   quality (> 98.5% perfect index reads or PIR). More than 4 billion
   sequences were read in a total of 30 samples and with a minimum of 15
   million sequence depth per sample (Figure [139]S2). In case of
   differential gene expression study, the transcripts that were passed
   through p value significance (< 0.05) and FC (> 1.5) filters were
   considered as significant changes in gene expression from a total of
   > 40,000 transcripts expressed in PHHs.

Pathway enrichment analysis

   Pathway enrichment data analysis was performed using Ingenuity Pathway
   Analysis or IPA (Qiagen Inc.,
   [140]https://www.qiagenbioinformatics.com/products/ingenuitypathway-Ana
   lysis) according to the standard protocols as previously
   reported^[141]50. In this, we overlaid a list of rifamycin RGs on more
   than 500 cellular and metabolic pathways available in IPA to identify
   various hepatic and metabolic pathways regulated during rifampin,
   rifabutin and rifapentine treatments in PHHs as previously
   described^[142]51. UNMC has a license to IPA software and subscribers
   can use it to generate images of pathways and gene networks that can be
   published without any consent from Qiagen. IPA provided a list of
   regulated pathways based on the involvement of differentially expressed
   transcripts from a particular dataset. ‘Core analysis’ was performed on
   the list of significantly up and down regulated differentially
   expressed drug responsive genes (0.05p and 1.5 FC) to interpret
   biological pathways according to the standard protocol^[143]51. We set
   several parameters, including, ‘p value’ significance, ‘ratio’ of total
   number of genes involved in a pathway regulated by RRGs to a total
   number of genes known in a pathway and ‘z score’ that strongly predicts
   significantly regulated pathways. Pathways that were passed through the
   filters of < 0.01 p value significance, 0.1 ratio that constitutes to a
   minimum involvement of 10 percent of differentially expressed genes in
   a particular pathway to a total number of pathway specific genes and
   positive activation z score of 2.0 that strongly predict the influence
   on a pathway were considered as significantly regulated pathways.

Transcriptional network and upstream regulator analysis (URA)

   Transcriptional network and functional analyses were generated through
   the use of IPA (Qiagen Inc., (Qiagen Inc.,
   [144]https://www.qiagenbioinformatics.com/products/ingenuitypathway-Ana
   lysis) according to the standard protocols as previously
   reported^[145]50. In a IPA core analysis of rifampin, rifabutin and
   rifapentine RGs, we compared rifamycin RG data sets to find a link to
   each of the drug responsive network on the commonly shared genes. Genes
   unrelated to metabolic pathways were removed from our analysis.
   Metabolic networks containing DMEs, drug transporters and TFs were
   separately analyzed. TFs involved in regulation of transcriptional
   network operated genes were identified and their significance and
   targets were identified by URA. URA of transcripts was performed to
   identify significantly (p = 0.01) regulated TFs either in the list of
   rifamycin RGs or based on prediction. TFs with significant Z score
   value (> 2.0) were predicted as activated or inhibited pathways based
   on the prediction of IPA software.

Statistical analysis

   Statistically significant differences in rifampin, rifabutin and
   rifapentine ICC in PHHs was performed between two groups using a
   student t test in Prism sofware (Graph Pad Sofware Inc, La Jolla, USA).
   A difference with > 0.05 p value between the two groups was considered
   significant. Significance analyses in differential gene expression,
   pathway and upstream regulator analyses were described in their
   respective sections above.

Supplementary information

   [146]Supplementary file1 (DOCX 1307 kb)^ (1.3MB, docx)

Acknowledgements